{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\xlunsi\Dropbox\VR utb reformer\DataAndPaper\Replication_log_file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 May 2019, 14:37:15
{txt}
{com}. /////////////////////////////////////////////////////////////////////////////
> /////////////////////////// Replication file ////////////////////////////////
> /////////////////////////////////////////////////////////////////////////////
> 
. // The following analyses were carried out using Stata/SE 15.1
. 
. // This script constructs the analysis dataset and reproduces the results in 
. // the paper "Does Deliberative Education Increase Civic Competence?
. // Results from a Field Experiment"
. 
. // Uncomment the following line if you have not installed the "balancetable" command
. // ssc install balancetable
. // Uncomment the following line if you have not installed the "ritest" command
. // net install ritest
. 
. set more off
{txt}
{com}. clear
{txt}
{com}. 
. // Setting the working directory.
. // Note: The directory need to include the datasets "Deliberation_Data.dta" and
. // "Deliberation_Grades_Data.dta" and one folder named "Tables".
. 
. cd "C:/Users/xlunsi/Dropbox/VR utb reformer/DataAndPaper"
{res}C:\Users\xlunsi\Dropbox\VR utb reformer\DataAndPaper
{txt}
{com}. 
. // Load data
. use  "Deliberation_Data.dta", clear
{txt}
{com}. 
. 
. ///// Preparation of the data /////
> 
. // Recoding outcome variables and creating indices
. 
. // Outcome variable: Interest
. 
. // Wave 1
. recode Samhallsintresse (1=1) (2=.666) (3=.333) (4=0)  (5/999=.), gen(interest1)
{txt}(1027 differences between Samhallsintresse and interest1)

{com}. 
. // Wave 2
. recode Samhallsintresse2 (1=1) (2=.666) (3=.333) (4=0)  (5/999=.), gen(interest2)
{txt}(955 differences between Samhallsintresse2 and interest2)

{com}. 
. // Wave 3
. recode Samhallsintresse3 (1=1) (2=.666) (3=.333) (4=0)  (5/999=.), gen(interest3)
{txt}(773 differences between Samhallsintresse3 and interest3)

{com}. 
. 
. // Outcome variable: Values
. 
. // Wave 1
. gen values1 = values1a  + values1c + values1d + values1e + values1f + values1g  + values1i + values1j 
{txt}(204 missing values generated)

{com}. replace values1 = (values1 - 8 ) / 24
{txt}(1,235 real changes made)

{com}. 
. // Wave 2
. gen values2 = values2a  + values2c + values2d + values2e + values2f + values2g  + values2i + values2j 
{txt}(281 missing values generated)

{com}. replace values2 = (values2 - 8 ) / 24
{txt}(1,158 real changes made)

{com}. alpha values2a    values2c  values2d  values2e  values2f  values2g    values2i  values2j     

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0598763
{txt}Number of items in the scale:{col 34}{res}        8
{txt}Scale reliability coefficient:{col 34}{res}   0.5672
{txt}
{com}. 
. // Wave 3
. gen values3 = values3a  + values3c + values3d + values3e + values3f + values3g  + values3i + values3j 
{txt}(518 missing values generated)

{com}. replace values3 = (values3 - 8 ) / 24
{txt}(921 real changes made)

{com}. alpha values3a    values3c  values3d  values3e  values3f  values3g    values3i  values3j     

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0685207
{txt}Number of items in the scale:{col 34}{res}        8
{txt}Scale reliability coefficient:{col 34}{res}   0.5873
{txt}
{com}. 
. // Outcome varaible: Discussions
. 
. // Wave 1
. gen talk1 = talk1a + talk1b + talk1c 
{txt}(149 missing values generated)

{com}. replace talk1 = (talk1 - 3 ) / 9
{txt}(1,290 real changes made)

{com}. 
. // Wave 2
. gen talk2 = talk2a + talk2b + talk2c 
{txt}(238 missing values generated)

{com}. replace talk2 = (talk2 - 3 ) / 9
{txt}(1,201 real changes made)

{com}. alpha talk2a  talk2b  talk2c 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2962308
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7184
{txt}
{com}. 
. // Wave 3
. gen talk3 = talk3a + talk3b + talk3c 
{txt}(482 missing values generated)

{com}. replace talk3 = (talk3 - 3 ) / 9
{txt}(957 real changes made)

{com}. alpha  talk3a talk3b talk3c 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .3104611
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7317
{txt}
{com}. 
. // Outcome variable: Knowledge
. 
. // Wave 1
. gen knowledge1 = knowledge1a + knowledge1b + knowledge1c + knowledge1d + knowledge1e
{txt}(516 missing values generated)

{com}. replace knowledge1 =. if knowledge1a == . & knowledge1b == . & knowledge1c ==. ///
> & knowledge1d == . & knowledge1e == . 
{txt}(0 real changes made)

{com}. 
. // Wave 2
. gen knowledge2 =  knowledge2a + knowledge2b + knowledge2c + knowledge2d + ///
> knowledge2e + knowledge2f + knowledge2g
{txt}(543 missing values generated)

{com}. alpha  knowledge2a  knowledge2b  knowledge2c  knowledge2d knowledge2e ///
> knowledge2f knowledge2g

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0177094
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.3867
{txt}
{com}. replace knowledge2  =.  if knowledge2a == . & knowledge2b == . & knowledge2c ==. ///
> & knowledge2d == . & knowledge2e == .  & knowledge2f == . & knowledge2g == . 
{txt}(0 real changes made)

{com}. 
. // Wave 3
. gen knowledge3 =  knowledge3a + knowledge3b + knowledge3c + knowledge3d + ///
> knowledge3e + knowledge3f + knowledge3g
{txt}(639 missing values generated)

{com}. replace knowledge3 =.  if knowledge3a == . & knowledge3b == . & knowledge3c ==. ///
> & knowledge3d == . & knowledge3e == .  & knowledge3f == . & knowledge3g == . 
{txt}(0 real changes made)

{com}. alpha knowledge3a  knowledge3b  knowledge3c knowledge3d knowledge3e knowledge3f knowledge3g

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240988
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.4848
{txt}
{com}. 
. 
. // Outcome variable: Knowledge, alternative coding
. 
. // Wave 1
. egen xknowledge1=rowtotal(xknowledge1a  xknowledge1b xknowledge1c xknowledge1d xknowledge1e)
{txt}
{com}. replace xknowledge1  =. if knowledge1a == . & knowledge1b == . & knowledge1c ==. ///
> & knowledge1d == . & knowledge1e == . 
{txt}(169 real changes made, 169 to missing)

{com}. 
. // Wave 2
. egen xknowledge2 = rowtotal(xknowledge2a  xknowledge2b  xknowledge2c ///
> xknowledge2d  xknowledge2e  xknowledge2f  xknowledge2g)
{txt}
{com}. replace xknowledge2  =.  if knowledge2a == . & knowledge2b == . & knowledge2c ==. ///
> & knowledge2d == . & knowledge2e == .  & knowledge2f == . & knowledge2g == . 
{txt}(226 real changes made, 226 to missing)

{com}. alpha  xknowledge2a  xknowledge2b  xknowledge2c  xknowledge2d xknowledge2e ///
> xknowledge2f xknowledge2g

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0177094
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.3867
{txt}
{com}. 
. // Wave 3
. egen xknowledge3 = rowtotal(xknowledge3a  xknowledge3b  xknowledge3c ///
>  xknowledge3d  xknowledge3e  xknowledge3f  xknowledge3g)
{txt}
{com}. replace xknowledge3   =.  if knowledge3a == . & knowledge3b == . & knowledge3c ==. ///
> & knowledge3d == . & knowledge3e == .  & knowledge3f == . & knowledge3g == .
{txt}(462 real changes made, 462 to missing)

{com}. alpha xknowledge3a  xknowledge3b  xknowledge3c xknowledge3d ///
>  xknowledge3e xknowledge3f xknowledge3g

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240988
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.4848
{txt}
{com}. 
. // Create cimate indicator used for table A3 in appendix 4
. gen climate = climateQ1 + climateQ2 + climateQ3 + climateQ4 + climateQ5 + climateQ6 ///
> + climateQ + climateQ8 + climateQ9 + climateQ10 + climateQ11
{txt}(295 missing values generated)

{com}. 
. replace climate = (climate -11) /33
{txt}(1,144 real changes made)

{com}. 
. 
. // Label variables
. label variable values2 "Values"
{txt}
{com}. label variable interest2 "Interest"
{txt}
{com}. label variable knowledge2 "Knowledge"
{txt}
{com}. label variable xknowledge2 "Knowledge alternative coding"
{txt}
{com}. label variable talk2 "Discussions" 
{txt}
{com}. label variable values3 "Values"
{txt}
{com}. label variable interest3 "Interest"
{txt}
{com}. label variable knowledge3 "Knowledge"
{txt}
{com}. label variable xknowledge3 "Knowledge alternative coding"
{txt}
{com}. label variable talk3 "Discussions"
{txt}
{com}. label variable climate "Classroom climate Index"
{txt}
{com}. 
. 
. ////// Analysis of the data //////
> 
. 
. // Reconstructing table 1 in main paper //
. 
. // OLS model with baseline level of outcome and other control variables included 
. // Directly after experiment 
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}.  eststo: reg `k'2 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt}  3{com}. gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}     1,092
                                                {txt}F(10, 58)         =  {res}    50.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4266
                                                {txt}Root MSE          =    {res} .18645

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0121023{col 29}{space 2} .0124055{col 40}{space 1}   -0.98{col 49}{space 3}0.333{col 57}{space 4}-.0369347{col 70}{space 3} .0127301
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6647927{col 29}{space 2} .0323032{col 40}{space 1}   20.58{col 49}{space 3}0.000{col 57}{space 4} .6001308{col 70}{space 3} .7294546
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0176045{col 29}{space 2} .0073662{col 40}{space 1}    2.39{col 49}{space 3}0.020{col 57}{space 4} .0028595{col 70}{space 3} .0323495
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0178028{col 29}{space 2} .0346679{col 40}{space 1}    0.51{col 49}{space 3}0.610{col 57}{space 4}-.0515926{col 70}{space 3} .0871982
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0149713{col 29}{space 2} .0371149{col 40}{space 1}   -0.40{col 49}{space 3}0.688{col 57}{space 4}-.0892649{col 70}{space 3} .0593223
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0166893{col 29}{space 2} .0246419{col 40}{space 1}   -0.68{col 49}{space 3}0.501{col 57}{space 4}-.0660155{col 70}{space 3} .0326369
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0329415{col 29}{space 2} .0344341{col 40}{space 1}   -0.96{col 49}{space 3}0.343{col 57}{space 4}-.1018688{col 70}{space 3} .0359858
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0476514{col 29}{space 2} .0505185{col 40}{space 1}   -0.94{col 49}{space 3}0.349{col 57}{space 4} -.148775{col 70}{space 3} .0534723
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0077942{col 29}{space 2} .0230439{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.0539216{col 70}{space 3} .0383333
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0145644{col 29}{space 2}  .011191{col 40}{space 1}    1.30{col 49}{space 3}0.198{col 57}{space 4}-.0078367{col 70}{space 3} .0369656
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1858754{col 29}{space 2} .0303479{col 40}{space 1}    6.12{col 49}{space 3}0.000{col 57}{space 4} .1251274{col 70}{space 3} .2466233
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       994
                                                {txt}F(10, 58)         =  {res}    57.27
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3878
                                                {txt}Root MSE          =    {res} .08494

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0034781{col 29}{space 2}  .007598{col 40}{space 1}   -0.46{col 49}{space 3}0.649{col 57}{space 4}-.0186871{col 70}{space 3} .0117309
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5991808{col 29}{space 2} .0276316{col 40}{space 1}   21.68{col 49}{space 3}0.000{col 57}{space 4} .5438702{col 70}{space 3} .6544913
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0045791{col 29}{space 2} .0037551{col 40}{space 1}    1.22{col 49}{space 3}0.228{col 57}{space 4}-.0029376{col 70}{space 3} .0120958
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0073874{col 29}{space 2} .0147186{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4} -.022075{col 70}{space 3} .0368498
{txt}Nordic Country  {c |}{col 17}{res}{space 2}  .010555{col 29}{space 2} .0169585{col 40}{space 1}    0.62{col 49}{space 3}0.536{col 57}{space 4}-.0233912{col 70}{space 3} .0445012
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0040722{col 29}{space 2}  .014613{col 40}{space 1}    0.28{col 49}{space 3}0.781{col 57}{space 4}-.0251789{col 70}{space 3} .0333233
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} -.014346{col 29}{space 2} .0140731{col 40}{space 1}   -1.02{col 49}{space 3}0.312{col 57}{space 4}-.0425163{col 70}{space 3} .0138244
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0567321{col 29}{space 2} .0228625{col 40}{space 1}   -2.48{col 49}{space 3}0.016{col 57}{space 4}-.1024964{col 70}{space 3}-.0109678
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0004789{col 29}{space 2} .0131712{col 40}{space 1}    0.04{col 49}{space 3}0.971{col 57}{space 4}-.0258862{col 70}{space 3} .0268439
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0125909{col 29}{space 2} .0059574{col 40}{space 1}    2.11{col 49}{space 3}0.039{col 57}{space 4} .0006658{col 70}{space 3} .0245159
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3056007{col 29}{space 2} .0267243{col 40}{space 1}   11.44{col 49}{space 3}0.000{col 57}{space 4} .2521062{col 70}{space 3} .3590951
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.792
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       661
                                                {txt}F(10, 58)         =  {res}    13.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2565
                                                {txt}Root MSE          =    {res} 1.3068

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0681706{col 29}{space 2} .1430078{col 40}{space 1}    0.48{col 49}{space 3}0.635{col 57}{space 4}-.2180906{col 70}{space 3} .3544318
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2}   .56781{col 29}{space 2}  .060483{col 40}{space 1}    9.39{col 49}{space 3}0.000{col 57}{space 4} .4467402{col 70}{space 3} .6888799
{txt}{space 10}books {c |}{col 17}{res}{space 2} .2248919{col 29}{space 2} .0606245{col 40}{space 1}    3.71{col 49}{space 3}0.000{col 57}{space 4} .1035387{col 70}{space 3} .3462451
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.1212849{col 29}{space 2} .3582094{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.8383188{col 70}{space 3} .5957491
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.1072696{col 29}{space 2} .2920684{col 40}{space 1}   -0.37{col 49}{space 3}0.715{col 57}{space 4} -.691908{col 70}{space 3} .4773688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.5182216{col 29}{space 2} .2978497{col 40}{space 1}   -1.74{col 49}{space 3}0.087{col 57}{space 4}-1.114433{col 70}{space 3} .0779894
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0811357{col 29}{space 2} .3667154{col 40}{space 1}    0.22{col 49}{space 3}0.826{col 57}{space 4}-.6529248{col 70}{space 3} .8151963
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .7500018{col 29}{space 2} .5458411{col 40}{space 1}    1.37{col 49}{space 3}0.175{col 57}{space 4}-.3426179{col 70}{space 3} 1.842621
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .4818495{col 29}{space 2} .3153484{col 40}{space 1}    1.53{col 49}{space 3}0.132{col 57}{space 4} -.149389{col 70}{space 3} 1.113088
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3696383{col 29}{space 2} .1051654{col 40}{space 1}   -3.51{col 49}{space 3}0.001{col 57}{space 4}-.5801497{col 70}{space 3}-.1591268
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.932468{col 29}{space 2} .2818377{col 40}{space 1}    6.86{col 49}{space 3}0.000{col 57}{space 4} 1.368308{col 70}{space 3} 2.496627
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.706
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,061
                                                {txt}F(10, 58)         =  {res}    88.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5059
                                                {txt}Root MSE          =    {res} .15121

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0088686{col 29}{space 2}  .009053{col 40}{space 1}    0.98{col 49}{space 3}0.331{col 57}{space 4} -.009253{col 70}{space 3} .0269902
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .7120993{col 29}{space 2} .0259006{col 40}{space 1}   27.49{col 49}{space 3}0.000{col 57}{space 4} .6602536{col 70}{space 3}  .763945
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0072404{col 29}{space 2} .0044375{col 40}{space 1}    1.63{col 49}{space 3}0.108{col 57}{space 4}-.0016423{col 70}{space 3} .0161231
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0001952{col 29}{space 2}  .028212{col 40}{space 1}    0.01{col 49}{space 3}0.995{col 57}{space 4}-.0562772{col 70}{space 3} .0566676
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0172613{col 29}{space 2} .0319376{col 40}{space 1}   -0.54{col 49}{space 3}0.591{col 57}{space 4}-.0811913{col 70}{space 3} .0466688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0147605{col 29}{space 2} .0207593{col 40}{space 1}   -0.71{col 49}{space 3}0.480{col 57}{space 4}-.0563147{col 70}{space 3} .0267938
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0056186{col 29}{space 2} .0298155{col 40}{space 1}   -0.19{col 49}{space 3}0.851{col 57}{space 4}-.0653007{col 70}{space 3} .0540635
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0256548{col 29}{space 2} .0388453{col 40}{space 1}   -0.66{col 49}{space 3}0.512{col 57}{space 4}-.1034121{col 70}{space 3} .0521025
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0016652{col 29}{space 2} .0210454{col 40}{space 1}   -0.08{col 49}{space 3}0.937{col 57}{space 4}-.0437922{col 70}{space 3} .0404618
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}  .011868{col 29}{space 2}  .010195{col 40}{space 1}    1.16{col 49}{space 3}0.249{col 57}{space 4}-.0085395{col 70}{space 3} .0322754
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1237926{col 29}{space 2} .0191645{col 40}{space 1}    6.46{col 49}{space 3}0.000{col 57}{space 4} .0854306{col 70}{space 3} .1621546
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\mainpaper1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\mainpaper1.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table 2 in main paper //
. 
. // OLS model with baseline level of outcome and other control variables included 
. // End of the year
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}.  eststo: reg `k'3 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt}  3{com}. gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}       867
                                                {txt}F(10, 49)         =  {res}    42.39
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3274
                                                {txt}Root MSE          =    {res} .20153

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0017879{col 29}{space 2} .0172916{col 40}{space 1}   -0.10{col 49}{space 3}0.918{col 57}{space 4}-.0365366{col 70}{space 3} .0329608
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .5905666{col 29}{space 2} .0323022{col 40}{space 1}   18.28{col 49}{space 3}0.000{col 57}{space 4} .5256529{col 70}{space 3} .6554804
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0196272{col 29}{space 2} .0092138{col 40}{space 1}    2.13{col 49}{space 3}0.038{col 57}{space 4} .0011114{col 70}{space 3}  .038143
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0268165{col 29}{space 2} .0499759{col 40}{space 1}    0.54{col 49}{space 3}0.594{col 57}{space 4}-.0736139{col 70}{space 3} .1272469
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0822787{col 29}{space 2}  .059805{col 40}{space 1}    1.38{col 49}{space 3}0.175{col 57}{space 4}-.0379039{col 70}{space 3} .2024614
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0318299{col 29}{space 2} .0433314{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.0552477{col 70}{space 3} .1189075
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0148803{col 29}{space 2}  .046758{col 40}{space 1}   -0.32{col 49}{space 3}0.752{col 57}{space 4} -.108844{col 70}{space 3} .0790834
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0590968{col 29}{space 2} .0645294{col 40}{space 1}   -0.92{col 49}{space 3}0.364{col 57}{space 4}-.1887734{col 70}{space 3} .0705798
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0149128{col 29}{space 2} .0396585{col 40}{space 1}   -0.38{col 49}{space 3}0.709{col 57}{space 4}-.0946095{col 70}{space 3} .0647839
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0014458{col 29}{space 2} .0131652{col 40}{space 1}   -0.11{col 49}{space 3}0.913{col 57}{space 4}-.0279023{col 70}{space 3} .0250108
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1933031{col 29}{space 2} .0340532{col 40}{space 1}    5.68{col 49}{space 3}0.000{col 57}{space 4} .1248708{col 70}{space 3} .2617355
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       783
                                                {txt}F(10, 49)         =  {res}    39.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2999
                                                {txt}Root MSE          =    {res} .09431

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0002943{col 29}{space 2} .0087908{col 40}{space 1}   -0.03{col 49}{space 3}0.973{col 57}{space 4}-.0179602{col 70}{space 3} .0173715
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5332502{col 29}{space 2} .0317772{col 40}{space 1}   16.78{col 49}{space 3}0.000{col 57}{space 4} .4693916{col 70}{space 3} .5971089
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0120606{col 29}{space 2} .0039864{col 40}{space 1}    3.03{col 49}{space 3}0.004{col 57}{space 4} .0040497{col 70}{space 3} .0200716
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0159259{col 29}{space 2} .0209714{col 40}{space 1}    0.76{col 49}{space 3}0.451{col 57}{space 4}-.0262177{col 70}{space 3} .0580696
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0034884{col 29}{space 2}  .017992{col 40}{space 1}   -0.19{col 49}{space 3}0.847{col 57}{space 4}-.0396447{col 70}{space 3} .0326679
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0115944{col 29}{space 2}  .015434{col 40}{space 1}    0.75{col 49}{space 3}0.456{col 57}{space 4}-.0194215{col 70}{space 3} .0426102
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0046553{col 29}{space 2} .0248911{col 40}{space 1}   -0.19{col 49}{space 3}0.852{col 57}{space 4}-.0546759{col 70}{space 3} .0453653
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0065758{col 29}{space 2} .0298807{col 40}{space 1}    0.22{col 49}{space 3}0.827{col 57}{space 4}-.0534717{col 70}{space 3} .0666232
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0049987{col 29}{space 2} .0185346{col 40}{space 1}    0.27{col 49}{space 3}0.789{col 57}{space 4} -.032248{col 70}{space 3} .0422454
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0237157{col 29}{space 2} .0068329{col 40}{space 1}    3.47{col 49}{space 3}0.001{col 57}{space 4} .0099845{col 70}{space 3} .0374469
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3358117{col 29}{space 2} .0303269{col 40}{space 1}   11.07{col 49}{space 3}0.000{col 57}{space 4} .2748674{col 70}{space 3} .3967559
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       553
                                                {txt}F(10, 49)         =  {res}     8.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1503
                                                {txt}Root MSE          =    {res} 1.4453

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .3070968{col 29}{space 2} .2013972{col 40}{space 1}    1.52{col 49}{space 3}0.134{col 57}{space 4}-.0976261{col 70}{space 3} .7118196
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .4250509{col 29}{space 2} .0677561{col 40}{space 1}    6.27{col 49}{space 3}0.000{col 57}{space 4} .2888899{col 70}{space 3} .5612119
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1368964{col 29}{space 2} .0792457{col 40}{space 1}    1.73{col 49}{space 3}0.090{col 57}{space 4}-.0223537{col 70}{space 3} .2961466
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .4298643{col 29}{space 2}  .422204{col 40}{space 1}    1.02{col 49}{space 3}0.314{col 57}{space 4}-.4185865{col 70}{space 3} 1.278315
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8181836{col 29}{space 2} .3903759{col 40}{space 1}    2.10{col 49}{space 3}0.041{col 57}{space 4} .0336938{col 70}{space 3} 1.602673
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .1422207{col 29}{space 2} .2824547{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.4253934{col 70}{space 3} .7098347
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .1109432{col 29}{space 2} .3758281{col 40}{space 1}    0.30{col 49}{space 3}0.769{col 57}{space 4}-.6443117{col 70}{space 3}  .866198
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .2766683{col 29}{space 2} .5857656{col 40}{space 1}    0.47{col 49}{space 3}0.639{col 57}{space 4}-.9004718{col 70}{space 3} 1.453808
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .2182893{col 29}{space 2} .2422651{col 40}{space 1}    0.90{col 49}{space 3}0.372{col 57}{space 4}-.2685607{col 70}{space 3} .7051394
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} -.154347{col 29}{space 2} .1290919{col 40}{space 1}   -1.20{col 49}{space 3}0.238{col 57}{space 4}-.4137668{col 70}{space 3} .1050728
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.408984{col 29}{space 2} .3098663{col 40}{space 1}    7.77{col 49}{space 3}0.000{col 57}{space 4} 1.786284{col 70}{space 3} 3.031683
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.168
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       839
                                                {txt}F(10, 49)         =  {res}    62.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3965
                                                {txt}Root MSE          =    {res} .16842

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0056919{col 29}{space 2} .0129306{col 40}{space 1}    0.44{col 49}{space 3}0.662{col 57}{space 4} -.020293{col 70}{space 3} .0316769
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2}  .641795{col 29}{space 2} .0281394{col 40}{space 1}   22.81{col 49}{space 3}0.000{col 57}{space 4} .5852467{col 70}{space 3} .6983433
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0102984{col 29}{space 2} .0048732{col 40}{space 1}    2.11{col 49}{space 3}0.040{col 57}{space 4} .0005054{col 70}{space 3} .0200914
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0230584{col 29}{space 2} .0456547{col 40}{space 1}    0.51{col 49}{space 3}0.616{col 57}{space 4}-.0686882{col 70}{space 3}  .114805
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0636618{col 29}{space 2} .0415008{col 40}{space 1}    1.53{col 49}{space 3}0.131{col 57}{space 4}-.0197372{col 70}{space 3} .1470608
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0148773{col 29}{space 2} .0310287{col 40}{space 1}    0.48{col 49}{space 3}0.634{col 57}{space 4}-.0474771{col 70}{space 3} .0772318
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0178378{col 29}{space 2} .0452468{col 40}{space 1}   -0.39{col 49}{space 3}0.695{col 57}{space 4}-.1087647{col 70}{space 3} .0730891
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0441212{col 29}{space 2}  .051209{col 40}{space 1}   -0.86{col 49}{space 3}0.393{col 57}{space 4}-.1470295{col 70}{space 3} .0587871
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0004367{col 29}{space 2}  .030916{col 40}{space 1}   -0.01{col 49}{space 3}0.989{col 57}{space 4}-.0625647{col 70}{space 3} .0616913
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0056101{col 29}{space 2} .0126993{col 40}{space 1}   -0.44{col 49}{space 3}0.661{col 57}{space 4}-.0311303{col 70}{space 3}   .01991
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1427326{col 29}{space 2} .0256349{col 40}{space 1}    5.57{col 49}{space 3}0.000{col 57}{space 4} .0912173{col 70}{space 3} .1942479
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.445
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\mainpaper2.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers  
{res}{txt}(output written to {browse  `"Tables\mainpaper2.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. // Reconstructing table A1 in appendix 2 //
. 
. // Randomization checks for treated vs un untreated classes
. ta mother, gen(mother)

      {txt}Mother's {c |}
    country of {c |}
        origin {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Outside Europe {c |}{res}        205       15.94       15.94
{txt}        Europe {c |}{res}        151       11.74       27.68
{txt}Nordic Country {c |}{res}         43        3.34       31.03
{txt}        Sweden {c |}{res}        887       68.97      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,286      100.00
{txt}
{com}. ta books, gen(books)

 {txt}Number of books at {c |}
               home {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
       Less than 50 {c |}{res}        360       28.26       28.26
{txt} Between 50 and 100 {c |}{res}        417       32.73       60.99
{txt}Between 200 and 500 {c |}{res}        361       28.34       89.32
{txt}      More than 500 {c |}{res}        136       10.68      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      1,274      100.00
{txt}
{com}. ta father, gen(father)

      {txt}Father's {c |}
    country of {c |}
        origin {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Outside Europe {c |}{res}        210       16.42       16.42
{txt}        Europe {c |}{res}        163       12.74       29.16
{txt}Nordic Country {c |}{res}         35        2.74       31.90
{txt}        Sweden {c |}{res}        871       68.10      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,279      100.00
{txt}
{com}. preserve
{txt}
{com}. collapse (mean) t mother1-mother4 father1-father4 books1-books4 female , by(classID)
{txt}
{com}. balancetable t female  books1-books4 mother1-mother4 father1-father4 using "Tables\rando.tex",replace
{res}{txt}
{com}. restore 
{txt}
{com}. 
. 
. // Reconstructing table A3 in appendix 4 //
. 
. // Manipulation check 
. eststo clear
{txt}
{com}. eststo: qui reg climate  t, vce(cl classID)
{txt}({res}est1{txt} stored)

{com}. esttab using "Tables\manipCheck.tex"  , label se         ///
>      varwidth(13) b(3)  nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("Manipulation checks")  nonumbers
{res}{txt}(output written to {browse  `"Tables\manipCheck.tex"'})

{com}. eststo clear
{txt}
{com}. alpha climateQ1 climateQ2 climateQ3 climateQ4 climateQ5 climateQ6 ///
> climateQ climateQ8 climateQ9 climateQ10 climateQ11

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .1411759
{txt}Number of items in the scale:{col 34}{res}       11
{txt}Scale reliability coefficient:{col 34}{res}   0.7972
{txt}
{com}. 
. 
. // Reconstructing table A5 in appendix 7 //
. 
. // OLS models i) unadjusted, ii) adjusted for baseline level of outcome and
. // iii) adjusted for baseline level of outcome & other control variables included.
. 
. // Directly after experiment, Interest & Values
.         foreach k in interest values  {c -(}
{txt}  2{com}.          eststo: reg `k'2 t    , vce(cl classID)
{txt}  3{com}.          gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}. 
.  eststo: reg `k'2 t  `k'1   , vce(cl classID)
{txt}  7{com}.  gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}. 
.  eststo: reg `k'2 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt} 11{com}.   gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}     1,218
                                                {txt}F(1, 58)          =  {res}     0.74
                                                {txt}Prob > F          = {res}    0.3937
                                                {txt}R-squared         = {res}    0.0012
                                                {txt}Root MSE          =    {res} .24386

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   interest2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0167306{col 26}{space 2} .0194679{col 37}{space 1}    0.86{col 46}{space 3}0.394{col 54}{space 4}-.0222387{col 67}{space 3} .0556998
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6416364{col 26}{space 2} .0140633{col 37}{space 1}   45.62{col 46}{space 3}0.000{col 54}{space 4} .6134856{col 67}{space 3} .6697872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.642
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,137
                                                {txt}F(2, 58)          =  {res}   245.65
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4240
                                                {txt}Root MSE          =    {res} .18542

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   interest2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}-.0092071{col 26}{space 2} .0123289{col 37}{space 1}   -0.75{col 46}{space 3}0.458{col 54}{space 4} -.033886{col 67}{space 3} .0154719
{txt}{space 3}interest1 {c |}{col 14}{res}{space 2} .6731515{col 26}{space 2} .0305393{col 37}{space 1}   22.04{col 46}{space 3}0.000{col 54}{space 4} .6120204{col 67}{space 3} .7342826
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2056302{col 26}{space 2} .0241304{col 37}{space 1}    8.52{col 46}{space 3}0.000{col 54}{space 4}  .157328{col 67}{space 3} .2539323
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,092
                                                {txt}F(10, 58)         =  {res}    50.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4266
                                                {txt}Root MSE          =    {res} .18645

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0121023{col 29}{space 2} .0124055{col 40}{space 1}   -0.98{col 49}{space 3}0.333{col 57}{space 4}-.0369347{col 70}{space 3} .0127301
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6647927{col 29}{space 2} .0323032{col 40}{space 1}   20.58{col 49}{space 3}0.000{col 57}{space 4} .6001308{col 70}{space 3} .7294546
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0176045{col 29}{space 2} .0073662{col 40}{space 1}    2.39{col 49}{space 3}0.020{col 57}{space 4} .0028595{col 70}{space 3} .0323495
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0178028{col 29}{space 2} .0346679{col 40}{space 1}    0.51{col 49}{space 3}0.610{col 57}{space 4}-.0515926{col 70}{space 3} .0871982
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0149713{col 29}{space 2} .0371149{col 40}{space 1}   -0.40{col 49}{space 3}0.688{col 57}{space 4}-.0892649{col 70}{space 3} .0593223
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0166893{col 29}{space 2} .0246419{col 40}{space 1}   -0.68{col 49}{space 3}0.501{col 57}{space 4}-.0660155{col 70}{space 3} .0326369
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0329415{col 29}{space 2} .0344341{col 40}{space 1}   -0.96{col 49}{space 3}0.343{col 57}{space 4}-.1018688{col 70}{space 3} .0359858
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0476514{col 29}{space 2} .0505185{col 40}{space 1}   -0.94{col 49}{space 3}0.349{col 57}{space 4} -.148775{col 70}{space 3} .0534723
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0077942{col 29}{space 2} .0230439{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.0539216{col 70}{space 3} .0383333
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0145644{col 29}{space 2}  .011191{col 40}{space 1}    1.30{col 49}{space 3}0.198{col 57}{space 4}-.0078367{col 70}{space 3} .0369656
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1858754{col 29}{space 2} .0303479{col 40}{space 1}    6.12{col 49}{space 3}0.000{col 57}{space 4} .1251274{col 70}{space 3} .2466233
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,158
                                                {txt}F(1, 58)          =  {res}     1.32
                                                {txt}Prob > F          = {res}    0.2561
                                                {txt}R-squared         = {res}    0.0036
                                                {txt}Root MSE          =    {res} .10821

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     values2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0129598{col 26}{space 2} .0112999{col 37}{space 1}    1.15{col 46}{space 3}0.256{col 54}{space 4}-.0096594{col 67}{space 3}  .035579
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7901515{col 26}{space 2} .0095297{col 37}{space 1}   82.91{col 46}{space 3}0.000{col 54}{space 4} .7710758{col 67}{space 3} .8092273
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.790
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,032
                                                {txt}F(2, 58)          =  {res}   278.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3735
                                                {txt}Root MSE          =    {res} .08569

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     values2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} -.002625{col 26}{space 2}  .007534{col 37}{space 1}   -0.35{col 46}{space 3}0.729{col 54}{space 4} -.017706{col 67}{space 3}  .012456
{txt}{space 5}values1 {c |}{col 14}{res}{space 2} .6176385{col 26}{space 2} .0264896{col 37}{space 1}   23.32{col 46}{space 3}0.000{col 54}{space 4} .5646139{col 67}{space 3}  .670663
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3092514{col 26}{space 2} .0224246{col 37}{space 1}   13.79{col 46}{space 3}0.000{col 54}{space 4} .2643636{col 67}{space 3} .3541392
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.793
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       994
                                                {txt}F(10, 58)         =  {res}    57.27
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3878
                                                {txt}Root MSE          =    {res} .08494

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0034781{col 29}{space 2}  .007598{col 40}{space 1}   -0.46{col 49}{space 3}0.649{col 57}{space 4}-.0186871{col 70}{space 3} .0117309
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5991808{col 29}{space 2} .0276316{col 40}{space 1}   21.68{col 49}{space 3}0.000{col 57}{space 4} .5438702{col 70}{space 3} .6544913
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0045791{col 29}{space 2} .0037551{col 40}{space 1}    1.22{col 49}{space 3}0.228{col 57}{space 4}-.0029376{col 70}{space 3} .0120958
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0073874{col 29}{space 2} .0147186{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4} -.022075{col 70}{space 3} .0368498
{txt}Nordic Country  {c |}{col 17}{res}{space 2}  .010555{col 29}{space 2} .0169585{col 40}{space 1}    0.62{col 49}{space 3}0.536{col 57}{space 4}-.0233912{col 70}{space 3} .0445012
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0040722{col 29}{space 2}  .014613{col 40}{space 1}    0.28{col 49}{space 3}0.781{col 57}{space 4}-.0251789{col 70}{space 3} .0333233
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} -.014346{col 29}{space 2} .0140731{col 40}{space 1}   -1.02{col 49}{space 3}0.312{col 57}{space 4}-.0425163{col 70}{space 3} .0138244
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0567321{col 29}{space 2} .0228625{col 40}{space 1}   -2.48{col 49}{space 3}0.016{col 57}{space 4}-.1024964{col 70}{space 3}-.0109678
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0004789{col 29}{space 2} .0131712{col 40}{space 1}    0.04{col 49}{space 3}0.971{col 57}{space 4}-.0258862{col 70}{space 3} .0268439
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0125909{col 29}{space 2} .0059574{col 40}{space 1}    2.11{col 49}{space 3}0.039{col 57}{space 4} .0006658{col 70}{space 3} .0245159
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3056007{col 29}{space 2} .0267243{col 40}{space 1}   11.44{col 49}{space 3}0.000{col 57}{space 4} .2521062{col 70}{space 3} .3590951
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.792
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\reg1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\reg1.tex"'})

{com}.         eststo clear
{txt}
{com}.         
. 
. // Reconstructing table A6 in appendix 7 //
. 
. // Directly after experiment, Knowledge & Discussions
.         foreach k in knowledge talk  {c -(}
{txt}  2{com}.          eststo: reg `k'2 t    , vce(cl classID)
{txt}  3{com}.            gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: reg `k'2 t  `k'1   , vce(cl classID)
{txt}  7{com}.    gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: reg `k'2 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt} 11{com}. gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}       896
                                                {txt}F(1, 58)          =  {res}     3.05
                                                {txt}Prob > F          = {res}    0.0859
                                                {txt}R-squared         = {res}    0.0101
                                                {txt}Root MSE          =    {res} 1.5081

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  knowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3047148{col 26}{space 2} .1744048{col 37}{space 1}    1.75{col 46}{space 3}0.086{col 54}{space 4}-.0443944{col 67}{space 3}  .653824
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.631455{col 26}{space 2} .1382055{col 37}{space 1}   26.28{col 46}{space 3}0.000{col 54}{space 4} 3.354807{col 67}{space 3} 3.908104
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.631
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       686
                                                {txt}F(2, 58)          =  {res}    49.83
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2231
                                                {txt}Root MSE          =    {res}  1.335

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  knowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0235017{col 26}{space 2} .1553103{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4}-.2873856{col 67}{space 3} .3343889
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} .6054641{col 26}{space 2} .0608065{col 37}{space 1}    9.96{col 46}{space 3}0.000{col 54}{space 4} .4837467{col 67}{space 3} .7271814
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.150806{col 26}{space 2} .1931412{col 37}{space 1}   11.14{col 46}{space 3}0.000{col 54}{space 4} 1.764192{col 67}{space 3} 2.537421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.751
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       661
                                                {txt}F(10, 58)         =  {res}    13.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2565
                                                {txt}Root MSE          =    {res} 1.3068

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0681706{col 29}{space 2} .1430078{col 40}{space 1}    0.48{col 49}{space 3}0.635{col 57}{space 4}-.2180906{col 70}{space 3} .3544318
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2}   .56781{col 29}{space 2}  .060483{col 40}{space 1}    9.39{col 49}{space 3}0.000{col 57}{space 4} .4467402{col 70}{space 3} .6888799
{txt}{space 10}books {c |}{col 17}{res}{space 2} .2248919{col 29}{space 2} .0606245{col 40}{space 1}    3.71{col 49}{space 3}0.000{col 57}{space 4} .1035387{col 70}{space 3} .3462451
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.1212849{col 29}{space 2} .3582094{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.8383188{col 70}{space 3} .5957491
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.1072696{col 29}{space 2} .2920684{col 40}{space 1}   -0.37{col 49}{space 3}0.715{col 57}{space 4} -.691908{col 70}{space 3} .4773688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.5182216{col 29}{space 2} .2978497{col 40}{space 1}   -1.74{col 49}{space 3}0.087{col 57}{space 4}-1.114433{col 70}{space 3} .0779894
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0811357{col 29}{space 2} .3667154{col 40}{space 1}    0.22{col 49}{space 3}0.826{col 57}{space 4}-.6529248{col 70}{space 3} .8151963
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .7500018{col 29}{space 2} .5458411{col 40}{space 1}    1.37{col 49}{space 3}0.175{col 57}{space 4}-.3426179{col 70}{space 3} 1.842621
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .4818495{col 29}{space 2} .3153484{col 40}{space 1}    1.53{col 49}{space 3}0.132{col 57}{space 4} -.149389{col 70}{space 3} 1.113088
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3696383{col 29}{space 2} .1051654{col 40}{space 1}   -3.51{col 49}{space 3}0.001{col 57}{space 4}-.5801497{col 70}{space 3}-.1591268
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.932468{col 29}{space 2} .2818377{col 40}{space 1}    6.86{col 49}{space 3}0.000{col 57}{space 4} 1.368308{col 70}{space 3} 2.496627
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.706
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,201
                                                {txt}F(1, 58)          =  {res}     1.25
                                                {txt}Prob > F          = {res}    0.2679
                                                {txt}R-squared         = {res}    0.0024
                                                {txt}Root MSE          =    {res} .21468

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       talk2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}  .021083{col 26}{space 2} .0188481{col 37}{space 1}    1.12{col 46}{space 3}0.268{col 54}{space 4}-.0166455{col 67}{space 3} .0588116
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4249614{col 26}{space 2} .0132449{col 37}{space 1}   32.08{col 46}{space 3}0.000{col 54}{space 4} .3984489{col 67}{space 3} .4514739
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.425
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,103
                                                {txt}F(2, 58)          =  {res}   428.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5085
                                                {txt}Root MSE          =    {res} .15021

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       talk2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0093357{col 26}{space 2} .0092818{col 37}{space 1}    1.01{col 46}{space 3}0.319{col 54}{space 4}-.0092437{col 67}{space 3} .0279152
{txt}{space 7}talk1 {c |}{col 14}{res}{space 2}  .716243{col 26}{space 2} .0251656{col 37}{space 1}   28.46{col 46}{space 3}0.000{col 54}{space 4} .6658686{col 67}{space 3} .7666175
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1304832{col 26}{space 2} .0110445{col 37}{space 1}   11.81{col 46}{space 3}0.000{col 54}{space 4} .1083751{col 67}{space 3} .1525912
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}     1,061
                                                {txt}F(10, 58)         =  {res}    88.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5059
                                                {txt}Root MSE          =    {res} .15121

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0088686{col 29}{space 2}  .009053{col 40}{space 1}    0.98{col 49}{space 3}0.331{col 57}{space 4} -.009253{col 70}{space 3} .0269902
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .7120993{col 29}{space 2} .0259006{col 40}{space 1}   27.49{col 49}{space 3}0.000{col 57}{space 4} .6602536{col 70}{space 3}  .763945
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0072404{col 29}{space 2} .0044375{col 40}{space 1}    1.63{col 49}{space 3}0.108{col 57}{space 4}-.0016423{col 70}{space 3} .0161231
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0001952{col 29}{space 2}  .028212{col 40}{space 1}    0.01{col 49}{space 3}0.995{col 57}{space 4}-.0562772{col 70}{space 3} .0566676
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0172613{col 29}{space 2} .0319376{col 40}{space 1}   -0.54{col 49}{space 3}0.591{col 57}{space 4}-.0811913{col 70}{space 3} .0466688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0147605{col 29}{space 2} .0207593{col 40}{space 1}   -0.71{col 49}{space 3}0.480{col 57}{space 4}-.0563147{col 70}{space 3} .0267938
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0056186{col 29}{space 2} .0298155{col 40}{space 1}   -0.19{col 49}{space 3}0.851{col 57}{space 4}-.0653007{col 70}{space 3} .0540635
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0256548{col 29}{space 2} .0388453{col 40}{space 1}   -0.66{col 49}{space 3}0.512{col 57}{space 4}-.1034121{col 70}{space 3} .0521025
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0016652{col 29}{space 2} .0210454{col 40}{space 1}   -0.08{col 49}{space 3}0.937{col 57}{space 4}-.0437922{col 70}{space 3} .0404618
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}  .011868{col 29}{space 2}  .010195{col 40}{space 1}    1.16{col 49}{space 3}0.249{col 57}{space 4}-.0085395{col 70}{space 3} .0322754
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1237926{col 29}{space 2} .0191645{col 40}{space 1}    6.46{col 49}{space 3}0.000{col 57}{space 4} .0854306{col 70}{space 3} .1621546
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\reg2.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\reg2.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table A7 in appendix 7 //
. 
. // End of the year, Interest & Values
.                 foreach k in interest values  {c -(}
{txt}  2{com}.          eststo: reg `k'3 t    , vce(cl classID)
{txt}  3{com}.          gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: reg `k'3 t  `k'1   , vce(cl classID)
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: reg `k'3 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k' 
{txt} 14{com}. 
. {c )-}

{txt}Linear regression                               Number of obs     = {res}       979
                                                {txt}F(1, 49)          =  {res}     0.52
                                                {txt}Prob > F          = {res}    0.4756
                                                {txt}R-squared         = {res}    0.0011
                                                {txt}Root MSE          =    {res} .24809

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   interest3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0166724{col 26}{space 2} .0231926{col 37}{space 1}    0.72{col 46}{space 3}0.476{col 54}{space 4}-.0299349{col 67}{space 3} .0632797
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6341731{col 26}{space 2} .0188553{col 37}{space 1}   33.63{col 46}{space 3}0.000{col 54}{space 4} .5962818{col 67}{space 3} .6720643
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       899
                                                {txt}F(2, 49)          =  {res}   206.26
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3203
                                                {txt}Root MSE          =    {res} .20208

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   interest3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0008492{col 26}{space 2} .0172907{col 37}{space 1}    0.05{col 46}{space 3}0.961{col 54}{space 4}-.0338978{col 67}{space 3} .0355962
{txt}{space 3}interest1 {c |}{col 14}{res}{space 2} .6031856{col 26}{space 2}  .029944{col 37}{space 1}   20.14{col 46}{space 3}0.000{col 54}{space 4} .5430108{col 67}{space 3} .6633603
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .242375{col 26}{space 2} .0237248{col 37}{space 1}   10.22{col 46}{space 3}0.000{col 54}{space 4} .1946982{col 67}{space 3} .2900518
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.635
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       867
                                                {txt}F(10, 49)         =  {res}    42.39
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3274
                                                {txt}Root MSE          =    {res} .20153

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0017879{col 29}{space 2} .0172916{col 40}{space 1}   -0.10{col 49}{space 3}0.918{col 57}{space 4}-.0365366{col 70}{space 3} .0329608
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .5905666{col 29}{space 2} .0323022{col 40}{space 1}   18.28{col 49}{space 3}0.000{col 57}{space 4} .5256529{col 70}{space 3} .6554804
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0196272{col 29}{space 2} .0092138{col 40}{space 1}    2.13{col 49}{space 3}0.038{col 57}{space 4} .0011114{col 70}{space 3}  .038143
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0268165{col 29}{space 2} .0499759{col 40}{space 1}    0.54{col 49}{space 3}0.594{col 57}{space 4}-.0736139{col 70}{space 3} .1272469
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0822787{col 29}{space 2}  .059805{col 40}{space 1}    1.38{col 49}{space 3}0.175{col 57}{space 4}-.0379039{col 70}{space 3} .2024614
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0318299{col 29}{space 2} .0433314{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.0552477{col 70}{space 3} .1189075
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0148803{col 29}{space 2}  .046758{col 40}{space 1}   -0.32{col 49}{space 3}0.752{col 57}{space 4} -.108844{col 70}{space 3} .0790834
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0590968{col 29}{space 2} .0645294{col 40}{space 1}   -0.92{col 49}{space 3}0.364{col 57}{space 4}-.1887734{col 70}{space 3} .0705798
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0149128{col 29}{space 2} .0396585{col 40}{space 1}   -0.38{col 49}{space 3}0.709{col 57}{space 4}-.0946095{col 70}{space 3} .0647839
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0014458{col 29}{space 2} .0131652{col 40}{space 1}   -0.11{col 49}{space 3}0.913{col 57}{space 4}-.0279023{col 70}{space 3} .0250108
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1933031{col 29}{space 2} .0340532{col 40}{space 1}    5.68{col 49}{space 3}0.000{col 57}{space 4} .1248708{col 70}{space 3} .2617355
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       921
                                                {txt}F(1, 49)          =  {res}     0.71
                                                {txt}Prob > F          = {res}    0.4036
                                                {txt}R-squared         = {res}    0.0023
                                                {txt}Root MSE          =    {res} .11212

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     values3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0107696{col 26}{space 2} .0127826{col 37}{space 1}    0.84{col 46}{space 3}0.404{col 54}{space 4}-.0149181{col 67}{space 3} .0364573
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8098757{col 26}{space 2} .0107711{col 37}{space 1}   75.19{col 46}{space 3}0.000{col 54}{space 4} .7882304{col 67}{space 3} .8315211
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       809
                                                {txt}F(2, 49)          =  {res}   148.44
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2718
                                                {txt}Root MSE          =    {res} .09517

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     values3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0023026{col 26}{space 2} .0094238{col 37}{space 1}    0.24{col 46}{space 3}0.808{col 54}{space 4}-.0166351{col 67}{space 3} .0212403
{txt}{space 5}values1 {c |}{col 14}{res}{space 2} .5640716{col 26}{space 2} .0342429{col 37}{space 1}   16.47{col 46}{space 3}0.000{col 54}{space 4} .4952578{col 67}{space 3} .6328854
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3629554{col 26}{space 2} .0282475{col 37}{space 1}   12.85{col 46}{space 3}0.000{col 54}{space 4} .3061899{col 67}{space 3}  .419721
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.811
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       783
                                                {txt}F(10, 49)         =  {res}    39.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2999
                                                {txt}Root MSE          =    {res} .09431

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0002943{col 29}{space 2} .0087908{col 40}{space 1}   -0.03{col 49}{space 3}0.973{col 57}{space 4}-.0179602{col 70}{space 3} .0173715
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5332502{col 29}{space 2} .0317772{col 40}{space 1}   16.78{col 49}{space 3}0.000{col 57}{space 4} .4693916{col 70}{space 3} .5971089
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0120606{col 29}{space 2} .0039864{col 40}{space 1}    3.03{col 49}{space 3}0.004{col 57}{space 4} .0040497{col 70}{space 3} .0200716
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0159259{col 29}{space 2} .0209714{col 40}{space 1}    0.76{col 49}{space 3}0.451{col 57}{space 4}-.0262177{col 70}{space 3} .0580696
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0034884{col 29}{space 2}  .017992{col 40}{space 1}   -0.19{col 49}{space 3}0.847{col 57}{space 4}-.0396447{col 70}{space 3} .0326679
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0115944{col 29}{space 2}  .015434{col 40}{space 1}    0.75{col 49}{space 3}0.456{col 57}{space 4}-.0194215{col 70}{space 3} .0426102
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0046553{col 29}{space 2} .0248911{col 40}{space 1}   -0.19{col 49}{space 3}0.852{col 57}{space 4}-.0546759{col 70}{space 3} .0453653
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0065758{col 29}{space 2} .0298807{col 40}{space 1}    0.22{col 49}{space 3}0.827{col 57}{space 4}-.0534717{col 70}{space 3} .0666232
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0049987{col 29}{space 2} .0185346{col 40}{space 1}    0.27{col 49}{space 3}0.789{col 57}{space 4} -.032248{col 70}{space 3} .0422454
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0237157{col 29}{space 2} .0068329{col 40}{space 1}    3.47{col 49}{space 3}0.001{col 57}{space 4} .0099845{col 70}{space 3} .0374469
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3358117{col 29}{space 2} .0303269{col 40}{space 1}   11.07{col 49}{space 3}0.000{col 57}{space 4} .2748674{col 70}{space 3} .3967559
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\reg3.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\reg3.tex"'})

{com}.         eststo clear
{txt}
{com}.         
. 
. // Reconstructing table A8 in appendix 7 //
. 
. // End of the year, Knowledge & Discussions
.         foreach k in knowledge talk  {c -(}
{txt}  2{com}.          eststo: reg `k'3 t    , vce(cl classID)
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: reg `k'3 t  `k'1   , vce(cl classID)
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: reg `k'3 t  `k'1 books i.mother i.father female  , vce(cl classID)
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. 
. {c )-}

{txt}Linear regression                               Number of obs     = {res}       800
                                                {txt}F(1, 49)          =  {res}     3.33
                                                {txt}Prob > F          = {res}    0.0740
                                                {txt}R-squared         = {res}    0.0162
                                                {txt}Root MSE          =    {res} 1.5594

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  knowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .4041505{col 26}{space 2} .2213365{col 37}{space 1}    1.83{col 46}{space 3}0.074{col 54}{space 4}-.0406418{col 67}{space 3} .8489428
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.090379{col 26}{space 2} .1547283{col 37}{space 1}   26.44{col 46}{space 3}0.000{col 54}{space 4} 3.779441{col 67}{space 3} 4.401317
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.090
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       572
                                                {txt}F(2, 49)          =  {res}    32.12
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1275
                                                {txt}Root MSE          =    {res} 1.4547

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  knowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .2896742{col 26}{space 2} .1987592{col 37}{space 1}    1.46{col 46}{space 3}0.151{col 54}{space 4}-.1097475{col 67}{space 3} .6890959
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} .4399572{col 26}{space 2} .0685974{col 37}{space 1}    6.41{col 46}{space 3}0.000{col 54}{space 4} .3021056{col 67}{space 3} .5778088
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.966634{col 26}{space 2} .1971689{col 37}{space 1}   15.05{col 46}{space 3}0.000{col 54}{space 4} 2.570408{col 67}{space 3}  3.36286
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.181
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       553
                                                {txt}F(10, 49)         =  {res}     8.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1503
                                                {txt}Root MSE          =    {res} 1.4453

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .3070968{col 29}{space 2} .2013972{col 40}{space 1}    1.52{col 49}{space 3}0.134{col 57}{space 4}-.0976261{col 70}{space 3} .7118196
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .4250509{col 29}{space 2} .0677561{col 40}{space 1}    6.27{col 49}{space 3}0.000{col 57}{space 4} .2888899{col 70}{space 3} .5612119
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1368964{col 29}{space 2} .0792457{col 40}{space 1}    1.73{col 49}{space 3}0.090{col 57}{space 4}-.0223537{col 70}{space 3} .2961466
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .4298643{col 29}{space 2}  .422204{col 40}{space 1}    1.02{col 49}{space 3}0.314{col 57}{space 4}-.4185865{col 70}{space 3} 1.278315
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8181836{col 29}{space 2} .3903759{col 40}{space 1}    2.10{col 49}{space 3}0.041{col 57}{space 4} .0336938{col 70}{space 3} 1.602673
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .1422207{col 29}{space 2} .2824547{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.4253934{col 70}{space 3} .7098347
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .1109432{col 29}{space 2} .3758281{col 40}{space 1}    0.30{col 49}{space 3}0.769{col 57}{space 4}-.6443117{col 70}{space 3}  .866198
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .2766683{col 29}{space 2} .5857656{col 40}{space 1}    0.47{col 49}{space 3}0.639{col 57}{space 4}-.9004718{col 70}{space 3} 1.453808
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .2182893{col 29}{space 2} .2422651{col 40}{space 1}    0.90{col 49}{space 3}0.372{col 57}{space 4}-.2685607{col 70}{space 3} .7051394
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} -.154347{col 29}{space 2} .1290919{col 40}{space 1}   -1.20{col 49}{space 3}0.238{col 57}{space 4}-.4137668{col 70}{space 3} .1050728
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.408984{col 29}{space 2} .3098663{col 40}{space 1}    7.77{col 49}{space 3}0.000{col 57}{space 4} 1.786284{col 70}{space 3} 3.031683
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.168
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       957
                                                {txt}F(1, 49)          =  {res}     0.33
                                                {txt}Prob > F          = {res}    0.5659
                                                {txt}R-squared         = {res}    0.0008
                                                {txt}Root MSE          =    {res}  .2177

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       talk3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0120363{col 26}{space 2}  .020824{col 37}{space 1}    0.58{col 46}{space 3}0.566{col 54}{space 4}-.0298111{col 67}{space 3} .0538837
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4377186{col 26}{space 2} .0174563{col 37}{space 1}   25.08{col 46}{space 3}0.000{col 54}{space 4} .4026388{col 67}{space 3} .4727984
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.438
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       869
                                                {txt}F(2, 49)          =  {res}   327.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3866
                                                {txt}Root MSE          =    {res}  .1696

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       talk3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0067606{col 26}{space 2} .0121904{col 37}{space 1}    0.55{col 46}{space 3}0.582{col 54}{space 4}-.0177369{col 67}{space 3} .0312581
{txt}{space 7}talk1 {c |}{col 14}{res}{space 2}  .649925{col 26}{space 2} .0254272{col 37}{space 1}   25.56{col 46}{space 3}0.000{col 54}{space 4} .5988271{col 67}{space 3} .7010229
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .171471{col 26}{space 2} .0151276{col 37}{space 1}   11.33{col 46}{space 3}0.000{col 54}{space 4} .1410708{col 67}{space 3} .2018711
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.446
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}       839
                                                {txt}F(10, 49)         =  {res}    62.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3965
                                                {txt}Root MSE          =    {res} .16842

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0056919{col 29}{space 2} .0129306{col 40}{space 1}    0.44{col 49}{space 3}0.662{col 57}{space 4} -.020293{col 70}{space 3} .0316769
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2}  .641795{col 29}{space 2} .0281394{col 40}{space 1}   22.81{col 49}{space 3}0.000{col 57}{space 4} .5852467{col 70}{space 3} .6983433
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0102984{col 29}{space 2} .0048732{col 40}{space 1}    2.11{col 49}{space 3}0.040{col 57}{space 4} .0005054{col 70}{space 3} .0200914
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0230584{col 29}{space 2} .0456547{col 40}{space 1}    0.51{col 49}{space 3}0.616{col 57}{space 4}-.0686882{col 70}{space 3}  .114805
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0636618{col 29}{space 2} .0415008{col 40}{space 1}    1.53{col 49}{space 3}0.131{col 57}{space 4}-.0197372{col 70}{space 3} .1470608
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0148773{col 29}{space 2} .0310287{col 40}{space 1}    0.48{col 49}{space 3}0.634{col 57}{space 4}-.0474771{col 70}{space 3} .0772318
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0178378{col 29}{space 2} .0452468{col 40}{space 1}   -0.39{col 49}{space 3}0.695{col 57}{space 4}-.1087647{col 70}{space 3} .0730891
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0441212{col 29}{space 2}  .051209{col 40}{space 1}   -0.86{col 49}{space 3}0.393{col 57}{space 4}-.1470295{col 70}{space 3} .0587871
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0004367{col 29}{space 2}  .030916{col 40}{space 1}   -0.01{col 49}{space 3}0.989{col 57}{space 4}-.0625647{col 70}{space 3} .0616913
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0056101{col 29}{space 2} .0126993{col 40}{space 1}   -0.44{col 49}{space 3}0.661{col 57}{space 4}-.0311303{col 70}{space 3}   .01991
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1427326{col 29}{space 2} .0256349{col 40}{space 1}    5.57{col 49}{space 3}0.000{col 57}{space 4} .0912173{col 70}{space 3} .1942479
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.445
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\reg4.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers  
{res}{txt}(output written to {browse  `"Tables\reg4.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table A9 in appendix 8 //
. 
. // Multilevel random-intercept models i) unadjusted, ii) adjusted for baseline
. // level of outcome and iii) adjusted for baseline level of outcome & other
. // control variables included.
. 
. // Directly after experiment, Interest & Values
.         foreach k in interest values {c -(}
{txt}  2{com}.          eststo: xtmixed `k'2 t    || classID : , var
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: xtmixed `k'2 t  `k'1   || classID : , var
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: xtmixed `k'2 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1.5892876}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1.5891132}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-1.5891131}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,218
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         7
{txt}{col 63}avg{col 67}={col 69}{res}      20.6
{txt}{col 63}max{col 67}={col 69}{res}        33

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     1.35
{txt}Log likelihood = {res}-1.5891131{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.2453

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   interest2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}  .021863{col 26}{space 2} .0188178{col 37}{space 1}    1.16{col 46}{space 3}0.245{col 54}{space 4}-.0150192{col 67}{space 3} .0587451
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6386061{col 26}{space 2} .0135045{col 37}{space 1}   47.29{col 46}{space 3}0.000{col 54}{space 4} .6121377{col 67}{space 3} .6650744
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0022909{col 44}  .000928{col 58} .0010357{col 70} .0050676
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0570457{col 44}  .002365{col 58} .0525938{col 70} .0618745
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}13.78{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0001
{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.642   583.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 305.01194}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 305.08767}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 305.08785}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 305.08785}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,137
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         6
{txt}{col 63}avg{col 67}={col 69}{res}      19.3
{txt}{col 63}max{col 67}={col 69}{res}        33

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   822.57
{txt}Log likelihood = {res} 305.08785{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   interest2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}-.0080897{col 26}{space 2} .0125473{col 37}{space 1}   -0.64{col 46}{space 3}0.519{col 54}{space 4}-.0326819{col 67}{space 3} .0165025
{txt}{space 3}interest1 {c |}{col 14}{res}{space 2} .6703591{col 26}{space 2} .0234245{col 37}{space 1}   28.62{col 46}{space 3}0.000{col 54}{space 4} .6244479{col 67}{space 3} .7162703
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2074087{col 26}{space 2} .0173486{col 37}{space 1}   11.96{col 46}{space 3}0.000{col 54}{space 4} .1734061{col 67}{space 3} .2414113
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0004954{col 44} .0004397{col 58}  .000087{col 70} .0028216
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0338005{col 44} .0014575{col 58} .0310613{col 70} .0367814
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}1.80{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0897
{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633   541.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 290.85573}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 290.94383}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 290.94409}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 290.94409}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,092
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         6
{txt}{col 63}avg{col 67}={col 69}{res}      18.5
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   797.68
{txt}Log likelihood = {res} 290.94409{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0111066{col 29}{space 2} .0127762{col 40}{space 1}   -0.87{col 49}{space 3}0.385{col 57}{space 4}-.0361475{col 70}{space 3} .0139343
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6624599{col 29}{space 2} .0242482{col 40}{space 1}   27.32{col 49}{space 3}0.000{col 57}{space 4} .6149342{col 70}{space 3} .7099855
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0179394{col 29}{space 2} .0062167{col 40}{space 1}    2.89{col 49}{space 3}0.004{col 57}{space 4} .0057549{col 70}{space 3} .0301239
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0194228{col 29}{space 2}  .034449{col 40}{space 1}    0.56{col 49}{space 3}0.573{col 57}{space 4}-.0480959{col 70}{space 3} .0869415
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0155997{col 29}{space 2} .0415996{col 40}{space 1}   -0.37{col 49}{space 3}0.708{col 57}{space 4}-.0971333{col 70}{space 3} .0659339
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0162375{col 29}{space 2} .0277043{col 40}{space 1}   -0.59{col 49}{space 3}0.558{col 57}{space 4}-.0705369{col 70}{space 3}  .038062
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0338817{col 29}{space 2}  .033928{col 40}{space 1}   -1.00{col 49}{space 3}0.318{col 57}{space 4}-.1003793{col 70}{space 3}  .032616
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0474771{col 29}{space 2}  .045405{col 40}{space 1}   -1.05{col 49}{space 3}0.296{col 57}{space 4}-.1364692{col 70}{space 3}  .041515
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0076626{col 29}{space 2}  .027599{col 40}{space 1}   -0.28{col 49}{space 3}0.781{col 57}{space 4}-.0617556{col 70}{space 3} .0464305
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0138849{col 29}{space 2} .0115442{col 40}{space 1}    1.20{col 49}{space 3}0.229{col 57}{space 4}-.0087412{col 70}{space 3} .0365111
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1864208{col 29}{space 2} .0246931{col 40}{space 1}    7.55{col 49}{space 3}0.000{col 57}{space 4} .1380232{col 70}{space 3} .2348184
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0004881{col 44} .0004579{col 58} .0000776{col 70} .0030693
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0339337{col 44} .0014956{col 58} .0311254{col 70} .0369955
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}1.58{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.1046
{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633   523.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 959.05527}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 959.05527}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,158
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         6
{txt}{col 63}avg{col 67}={col 69}{res}      19.6
{txt}{col 63}max{col 67}={col 69}{res}        33

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     1.48
{txt}Log likelihood = {res} 959.05527{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.2243

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     values2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0131363{col 26}{space 2} .0108093{col 37}{space 1}    1.22{col 46}{space 3}0.224{col 54}{space 4}-.0080495{col 67}{space 3}  .034322
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7871912{col 26}{space 2} .0077473{col 37}{space 1}  101.61{col 46}{space 3}0.000{col 54}{space 4} .7720067{col 67}{space 3} .8023756
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0011279{col 44} .0003128{col 58} .0006549{col 70} .0019425
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0105648{col 44} .0004503{col 58} .0097181{col 70} .0114853
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}52.27{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.790   550.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 1079.3479}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 1079.3482}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 1079.3482}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,032
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         3
{txt}{col 63}avg{col 67}={col 69}{res}      17.5
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   577.79
{txt}Log likelihood = {res} 1079.3482{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     values2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}-.0010604{col 26}{space 2} .0072616{col 37}{space 1}   -0.15{col 46}{space 3}0.884{col 54}{space 4}-.0152928{col 67}{space 3}  .013172
{txt}{space 5}values1 {c |}{col 14}{res}{space 2} .6062241{col 26}{space 2} .0252998{col 37}{space 1}   23.96{col 46}{space 3}0.000{col 54}{space 4} .5566373{col 67}{space 3} .6558108
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3170892{col 26}{space 2} .0203924{col 37}{space 1}   15.55{col 46}{space 3}0.000{col 54}{space 4} .2771208{col 67}{space 3} .3570576
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0003417{col 44} .0001404{col 58} .0001528{col 70} .0007644
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0069845{col 44} .0003163{col 58} .0063912{col 70} .0076327
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}13.14{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0001
{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.793   486.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 1053.5344}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 1053.5345}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 1053.5345}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       994
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         3
{txt}{col 63}avg{col 67}={col 69}{res}      16.8
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   588.02
{txt}Log likelihood = {res} 1053.5345{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        values2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0020524{col 29}{space 2} .0074732{col 40}{space 1}   -0.27{col 49}{space 3}0.784{col 57}{space 4}-.0166995{col 70}{space 3} .0125947
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5875667{col 29}{space 2} .0258672{col 40}{space 1}   22.71{col 49}{space 3}0.000{col 57}{space 4} .5368678{col 70}{space 3} .6382655
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0046527{col 29}{space 2} .0029507{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0011306{col 70}{space 3}  .010436
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0096862{col 29}{space 2} .0163349{col 40}{space 1}    0.59{col 49}{space 3}0.553{col 57}{space 4}-.0223296{col 70}{space 3} .0417021
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0128085{col 29}{space 2} .0201121{col 40}{space 1}    0.64{col 49}{space 3}0.524{col 57}{space 4}-.0266104{col 70}{space 3} .0522275
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0043489{col 29}{space 2} .0136025{col 40}{space 1}    0.32{col 49}{space 3}0.749{col 57}{space 4}-.0223115{col 70}{space 3} .0310093
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0136646{col 29}{space 2} .0162544{col 40}{space 1}   -0.84{col 49}{space 3}0.401{col 57}{space 4}-.0455226{col 70}{space 3} .0181934
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0560643{col 29}{space 2} .0217352{col 40}{space 1}   -2.58{col 49}{space 3}0.010{col 57}{space 4}-.0986644{col 70}{space 3}-.0134641
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0021671{col 29}{space 2} .0135713{col 40}{space 1}    0.16{col 49}{space 3}0.873{col 57}{space 4}-.0244321{col 70}{space 3} .0287663
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0117511{col 29}{space 2} .0056036{col 40}{space 1}    2.10{col 49}{space 3}0.036{col 57}{space 4} .0007683{col 70}{space 3}  .022734
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3118852{col 29}{space 2} .0216556{col 40}{space 1}   14.40{col 49}{space 3}0.000{col 57}{space 4}  .269441{col 70}{space 3} .3543294
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0003807{col 44} .0001505{col 58} .0001754{col 70} .0008263
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0067635{col 44} .0003127{col 58} .0061775{col 70} .0074051
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}14.78{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0001
{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.792   471.000
{txt}{hline 13}{c BT}{hline 20}

{com}. esttab using "Tables\mixed1_1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixed1_1.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table A10 in appendix 8 //
. 
. // Directly after experiment, Knowledge & Discussions
.                 foreach k in  knowledge talk {c -(}
{txt}  2{com}.          eststo: xtmixed `k'2 t    || classID : , var
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: xtmixed `k'2 t  `k'1   || classID : , var
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: xtmixed `k'2 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1615.7653}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1615.7653}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       896
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         3
{txt}{col 63}avg{col 67}={col 69}{res}      15.2
{txt}{col 63}max{col 67}={col 69}{res}        26

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     5.57
{txt}Log likelihood = {res}-1615.7653{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0182

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  knowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3888912{col 26}{space 2}  .164739{col 37}{space 1}    2.36{col 46}{space 3}0.018{col 54}{space 4} .0660086{col 67}{space 3} .7117738
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.56066{col 26}{space 2} .1186911{col 37}{space 1}   30.00{col 46}{space 3}0.000{col 54}{space 4} 3.328029{col 67}{space 3}  3.79329
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .2479358{col 44} .0717935{col 58} .1405598{col 70}  .437338
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.017843{col 44} .0984439{col 58} 1.833834{col 70} 2.220315
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}45.42{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.631   426.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -1158.266}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -1158.266}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       686
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        23

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   193.83
{txt}Log likelihood = {res} -1158.266{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  knowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0955552{col 26}{space 2} .1485788{col 37}{space 1}    0.64{col 46}{space 3}0.520{col 54}{space 4} -.195654{col 67}{space 3} .3867644
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} .5983808{col 26}{space 2} .0433477{col 37}{space 1}   13.80{col 46}{space 3}0.000{col 54}{space 4}  .513421{col 67}{space 3} .6833406
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.124937{col 26}{space 2}   .15626{col 37}{space 1}   13.60{col 46}{space 3}0.000{col 54}{space 4} 1.818673{col 67}{space 3} 2.431201
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .1635854{col 44} .0566635{col 58} .0829663{col 70}  .322543
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.607724{col 44}  .090384{col 58} 1.439985{col 70} 1.795001
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}23.70{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.751   305.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  -1101.12}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:  -1101.12}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       661
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.2
{txt}{col 63}max{col 67}={col 69}{res}        23

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   219.19
{txt}Log likelihood = {res}  -1101.12{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .1352684{col 29}{space 2} .1424999{col 40}{space 1}    0.95{col 49}{space 3}0.342{col 57}{space 4}-.1440264{col 70}{space 3} .4145631
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .5646296{col 29}{space 2} .0435265{col 40}{space 1}   12.97{col 49}{space 3}0.000{col 57}{space 4} .4793193{col 70}{space 3}   .64994
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1829709{col 29}{space 2} .0542746{col 40}{space 1}    3.37{col 49}{space 3}0.001{col 57}{space 4} .0765945{col 70}{space 3} .2893472
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0472465{col 29}{space 2} .3149066{col 40}{space 1}    0.15{col 49}{space 3}0.881{col 57}{space 4}-.5699591{col 70}{space 3} .6644521
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0111457{col 29}{space 2} .4400368{col 40}{space 1}   -0.03{col 49}{space 3}0.980{col 57}{space 4}-.8736021{col 70}{space 3} .8513107
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.4276457{col 29}{space 2} .2605546{col 40}{space 1}   -1.64{col 49}{space 3}0.101{col 57}{space 4}-.9383234{col 70}{space 3}  .083032
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}  .045259{col 29}{space 2} .3034508{col 40}{space 1}    0.15{col 49}{space 3}0.881{col 57}{space 4}-.5494936{col 70}{space 3} .6400117
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8249699{col 29}{space 2} .4430529{col 40}{space 1}    1.86{col 49}{space 3}0.063{col 57}{space 4}-.0433978{col 70}{space 3} 1.693338
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .5455166{col 29}{space 2} .2510863{col 40}{space 1}    2.17{col 49}{space 3}0.030{col 57}{space 4} .0533966{col 70}{space 3} 1.037637
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3400217{col 29}{space 2} .1037293{col 40}{space 1}   -3.28{col 49}{space 3}0.001{col 57}{space 4}-.5433275{col 70}{space 3}-.1367159
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.855413{col 29}{space 2} .2231636{col 40}{space 1}    8.31{col 49}{space 3}0.000{col 57}{space 4}  1.41802{col 70}{space 3} 2.292805
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .1377615{col 44} .0530742{col 58}  .064743{col 70} .2931318
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.544114{col 44} .0887778{col 58} 1.379559{col 70} 1.728299
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}16.28{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.706   296.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  157.9015}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:  157.9015}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,201
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         7
{txt}{col 63}avg{col 67}={col 69}{res}      20.4
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     1.91
{txt}Log likelihood = {res}  157.9015{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.1671

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       talk2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0255919{col 26}{space 2} .0185218{col 37}{space 1}    1.38{col 46}{space 3}0.167{col 54}{space 4}-.0107101{col 67}{space 3} .0618939
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4230141{col 26}{space 2} .0132736{col 37}{space 1}   31.87{col 46}{space 3}0.000{col 54}{space 4} .3969983{col 67}{space 3} .4490299
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0027702{col 44} .0009152{col 58} .0014498{col 70} .0052932
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0432457{col 44} .0018078{col 58} .0398437{col 70} .0469381
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}26.41{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.425   576.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  526.9415}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 527.41846}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 527.41879}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 527.41944}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 527.41944}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,103
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         6
{txt}{col 63}avg{col 67}={col 69}{res}      18.7
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}  1138.07
{txt}Log likelihood = {res} 527.41944{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       talk2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0094458{col 26}{space 2} .0091548{col 37}{space 1}    1.03{col 46}{space 3}0.302{col 54}{space 4}-.0084973{col 67}{space 3} .0273889
{txt}{space 7}talk1 {c |}{col 14}{res}{space 2} .7158491{col 26}{space 2} .0212668{col 37}{space 1}   33.66{col 46}{space 3}0.000{col 54}{space 4} .6741669{col 67}{space 3} .7575313
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1306627{col 26}{space 2} .0109127{col 37}{space 1}   11.97{col 46}{space 3}0.000{col 54}{space 4} .1092742{col 67}{space 3} .1520511
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33}  .000027{col 44} .0002465{col 58} 4.55e-13{col 70} 1599.788
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0224739{col 44} .0009868{col 58} .0206207{col 70} .0244937
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}0.01{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.4557
{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423   528.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 503.54011}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 504.34353}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 504.35142}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 504.35144}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,061
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         6
{txt}{col 63}avg{col 67}={col 69}{res}      18.0
{txt}{col 63}max{col 67}={col 69}{res}        29

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}  1086.12
{txt}Log likelihood = {res} 504.35144{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          talk2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0088686{col 29}{space 2} .0092766{col 40}{space 1}    0.96{col 49}{space 3}0.339{col 57}{space 4}-.0093132{col 70}{space 3} .0270504
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .7120993{col 29}{space 2} .0225428{col 40}{space 1}   31.59{col 49}{space 3}0.000{col 57}{space 4} .6679163{col 70}{space 3} .7562823
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0072404{col 29}{space 2} .0051637{col 40}{space 1}    1.40{col 49}{space 3}0.161{col 57}{space 4}-.0028803{col 70}{space 3}  .017361
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0001952{col 29}{space 2} .0280571{col 40}{space 1}    0.01{col 49}{space 3}0.994{col 57}{space 4}-.0547957{col 70}{space 3} .0551861
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0172613{col 29}{space 2} .0343943{col 40}{space 1}   -0.50{col 49}{space 3}0.616{col 57}{space 4}-.0846728{col 70}{space 3} .0501503
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0147605{col 29}{space 2} .0224693{col 40}{space 1}   -0.66{col 49}{space 3}0.511{col 57}{space 4}-.0587994{col 70}{space 3} .0292785
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0056186{col 29}{space 2}  .027565{col 40}{space 1}   -0.20{col 49}{space 3}0.838{col 57}{space 4}-.0596451{col 70}{space 3} .0484079
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0256548{col 29}{space 2} .0383861{col 40}{space 1}   -0.67{col 49}{space 3}0.504{col 57}{space 4}-.1008903{col 70}{space 3} .0495806
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0016652{col 29}{space 2} .0223823{col 40}{space 1}   -0.07{col 49}{space 3}0.941{col 57}{space 4}-.0455337{col 70}{space 3} .0422033
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}  .011868{col 29}{space 2} .0093126{col 40}{space 1}    1.27{col 49}{space 3}0.203{col 57}{space 4}-.0063844{col 70}{space 3} .0301203
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1237926{col 29}{space 2} .0177471{col 40}{space 1}    6.98{col 49}{space 3}0.000{col 57}{space 4} .0890089{col 70}{space 3} .1585763
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 2.58e-16{col 44} 1.41e-15{col 58} 5.74e-21{col 70} 1.16e-11
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0226276{col 44} .0009824{col 58} .0207817{col 70} .0246374
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}0.00{col 55}{txt}Prob >= chibar2 = {col 73}{res}1.0000
{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423   510.000
{txt}{hline 13}{c BT}{hline 20}

{com}. esttab using "Tables\mixed1_2.tex", keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixed1_2.tex"'})

{com}.         eststo clear
{txt}
{com}.  
. 
. // Reconstructing table A11 in appendix 8 //
. 
. // End of year, Interest & Values
.         foreach k in interest values {c -(}
{txt}  2{com}.          eststo: xtmixed `k'3 t    || classID : , var
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: xtmixed `k'3 t  `k'1   || classID : , var
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: xtmixed `k'3 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-17.150366}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-17.150276}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-17.150276}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       979
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         9
{txt}{col 63}avg{col 67}={col 69}{res}      19.6
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.58
{txt}Log likelihood = {res}-17.150276{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.4479

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   interest3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0164604{col 26}{space 2} .0216888{col 37}{space 1}    0.76{col 46}{space 3}0.448{col 54}{space 4}-.0260488{col 67}{space 3} .0589696
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .632155{col 26}{space 2} .0163487{col 37}{space 1}   38.67{col 46}{space 3}0.000{col 54}{space 4} .6001122{col 67}{space 3} .6641978
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0026017{col 44} .0011135{col 58} .0011245{col 70} .0060197
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0587717{col 44}  .002721{col 58} .0536734{col 70} .0643543
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}12.58{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0002
{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 165.54775}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 165.55704}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 165.55704}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       899
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      18.0
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   415.76
{txt}Log likelihood = {res} 165.55704{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   interest3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0012125{col 26}{space 2} .0165995{col 37}{space 1}    0.07{col 46}{space 3}0.942{col 54}{space 4} -.031322{col 67}{space 3}  .033747
{txt}{space 3}interest1 {c |}{col 14}{res}{space 2} .5991808{col 26}{space 2} .0294257{col 37}{space 1}   20.36{col 46}{space 3}0.000{col 54}{space 4} .5415074{col 67}{space 3} .6568542
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2435891{col 26}{space 2} .0229071{col 37}{space 1}   10.63{col 46}{space 3}0.000{col 54}{space 4} .1986921{col 67}{space 3} .2884861
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0010423{col 44} .0006604{col 58} .0003011{col 70} .0036086
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  .039664{col 44} .0019221{col 58} .0360702{col 70}  .043616
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}4.22{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0200
{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.635
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 165.69625}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 165.71332}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 165.71332}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       867
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      17.3
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   412.85
{txt}Log likelihood = {res} 165.71332{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0013634{col 29}{space 2} .0164861{col 40}{space 1}   -0.08{col 49}{space 3}0.934{col 57}{space 4}-.0336756{col 70}{space 3} .0309488
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .5867265{col 29}{space 2}  .030096{col 40}{space 1}   19.50{col 49}{space 3}0.000{col 57}{space 4} .5277393{col 70}{space 3} .6457136
{txt}{space 10}books {c |}{col 17}{res}{space 2}  .018337{col 29}{space 2} .0075663{col 40}{space 1}    2.42{col 49}{space 3}0.015{col 57}{space 4} .0035073{col 70}{space 3} .0331667
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0282321{col 29}{space 2} .0429316{col 40}{space 1}    0.66{col 49}{space 3}0.511{col 57}{space 4}-.0559124{col 70}{space 3} .1123765
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0870101{col 29}{space 2} .0503355{col 40}{space 1}    1.73{col 49}{space 3}0.084{col 57}{space 4}-.0116457{col 70}{space 3} .1856659
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0332382{col 29}{space 2}   .03371{col 40}{space 1}    0.99{col 49}{space 3}0.324{col 57}{space 4}-.0328322{col 70}{space 3} .0993087
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0179633{col 29}{space 2} .0422409{col 40}{space 1}   -0.43{col 49}{space 3}0.671{col 57}{space 4} -.100754{col 70}{space 3} .0648274
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0644962{col 29}{space 2} .0554987{col 40}{space 1}   -1.16{col 49}{space 3}0.245{col 57}{space 4}-.1732717{col 70}{space 3} .0442793
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0177965{col 29}{space 2} .0338107{col 40}{space 1}   -0.53{col 49}{space 3}0.599{col 57}{space 4}-.0840642{col 70}{space 3} .0484712
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0036069{col 29}{space 2}  .014066{col 40}{space 1}   -0.26{col 49}{space 3}0.798{col 57}{space 4}-.0311756{col 70}{space 3} .0239619
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1997576{col 29}{space 2}  .032396{col 40}{space 1}    6.17{col 49}{space 3}0.000{col 57}{space 4} .1362625{col 70}{space 3} .2632527
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0009277{col 44} .0006502{col 58} .0002349{col 70} .0036642
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0391769{col 44} .0019358{col 58} .0355608{col 70} .0431608
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}3.24{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0360
{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 728.76507}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 728.76507}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       921
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         8
{txt}{col 63}avg{col 67}={col 69}{res}      18.4
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     1.24
{txt}Log likelihood = {res} 728.76507{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.2663

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     values3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0141674{col 26}{space 2} .0127454{col 37}{space 1}    1.11{col 46}{space 3}0.266{col 54}{space 4}-.0108131{col 67}{space 3}  .039148
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8035344{col 26}{space 2} .0095975{col 37}{space 1}   83.72{col 46}{space 3}0.000{col 54}{space 4} .7847237{col 67}{space 3} .8223451
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33}  .001324{col 44} .0004128{col 58} .0007185{col 70} .0024395
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0113193{col 44} .0005434{col 58} .0103029{col 70}  .012436
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}38.58{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 760.09953}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 760.10126}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 760.10126}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       809
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      16.2
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   280.70
{txt}Log likelihood = {res} 760.10126{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     values3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0044989{col 26}{space 2}   .00883{col 37}{space 1}    0.51{col 46}{space 3}0.610{col 54}{space 4}-.0128075{col 67}{space 3} .0218054
{txt}{space 5}values1 {c |}{col 14}{res}{space 2} .5476099{col 26}{space 2} .0328282{col 37}{space 1}   16.68{col 46}{space 3}0.000{col 54}{space 4} .4832678{col 67}{space 3}  .611952
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3741985{col 26}{space 2} .0268241{col 37}{space 1}   13.95{col 46}{space 3}0.000{col 54}{space 4} .3216242{col 67}{space 3} .4267728
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0003764{col 44} .0001956{col 58}  .000136{col 70} .0010422
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0086594{col 44} .0004451{col 58} .0078295{col 70} .0095772
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}7.35{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0034
{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.811
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 745.32593}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:  745.3368}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:  745.3368}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       783
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      15.7
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   312.53
{txt}Log likelihood = {res}  745.3368{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        values3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0014702{col 29}{space 2} .0083991{col 40}{space 1}    0.18{col 49}{space 3}0.861{col 57}{space 4}-.0149917{col 70}{space 3} .0179321
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5241098{col 29}{space 2} .0336288{col 40}{space 1}   15.59{col 49}{space 3}0.000{col 57}{space 4} .4581986{col 70}{space 3} .5900209
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0119008{col 29}{space 2} .0037298{col 40}{space 1}    3.19{col 49}{space 3}0.001{col 57}{space 4} .0045905{col 70}{space 3} .0192111
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}  .015625{col 29}{space 2}  .020715{col 40}{space 1}    0.75{col 49}{space 3}0.451{col 57}{space 4}-.0249757{col 70}{space 3} .0562257
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0051929{col 29}{space 2} .0243198{col 40}{space 1}   -0.21{col 49}{space 3}0.831{col 57}{space 4}-.0528589{col 70}{space 3} .0424731
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0099377{col 29}{space 2} .0162208{col 40}{space 1}    0.61{col 49}{space 3}0.540{col 57}{space 4}-.0218546{col 70}{space 3} .0417299
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0025831{col 29}{space 2} .0205093{col 40}{space 1}   -0.13{col 49}{space 3}0.900{col 57}{space 4}-.0427807{col 70}{space 3} .0376144
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0108804{col 29}{space 2} .0275317{col 40}{space 1}    0.40{col 49}{space 3}0.693{col 57}{space 4}-.0430807{col 70}{space 3} .0648415
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0050395{col 29}{space 2} .0163971{col 40}{space 1}    0.31{col 49}{space 3}0.759{col 57}{space 4}-.0270983{col 70}{space 3} .0371772
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0229578{col 29}{space 2} .0069258{col 40}{space 1}    3.31{col 49}{space 3}0.001{col 57}{space 4} .0093835{col 70}{space 3} .0365321
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}   .34317{col 29}{space 2} .0280959{col 40}{space 1}   12.21{col 49}{space 3}0.000{col 57}{space 4}  .288103{col 70}{space 3}  .398237
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0002773{col 44} .0001787{col 58} .0000784{col 70} .0009807
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0085016{col 44} .0004452{col 58} .0076724{col 70} .0094205
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}4.07{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0218
{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\mixed2_1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixed2_1.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table A12 in appendix 8 //
. 
. // End of year, Knowledge & Discussions
.                 foreach k in  knowledge talk {c -(}
{txt}  2{com}.          eststo: xtmixed `k'3 t    || classID : , var
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: xtmixed `k'3 t  `k'1   || classID : , var
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: xtmixed `k'3 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1448.6321}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1448.6321}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       800
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         7
{txt}{col 63}avg{col 67}={col 69}{res}      16.0
{txt}{col 63}max{col 67}={col 69}{res}        29

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     3.40
{txt}Log likelihood = {res}-1448.6321{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0650

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  knowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3945721{col 26}{space 2} .2138352{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.0245371{col 67}{space 3} .8136813
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.026027{col 26}{space 2} .1604608{col 37}{space 1}   25.09{col 46}{space 3}0.000{col 54}{space 4} 3.711529{col 67}{space 3} 4.340524
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .4227275{col 44} .1125765{col 58} .2508287{col 70} .7124326
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.001709{col 44}  .103359{col 58} 1.809042{col 70} 2.214894
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}81.88{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.090
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1008.8935}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1008.8935}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       572
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}      11.4
{txt}{col 63}max{col 67}={col 69}{res}        25

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}    73.71
{txt}Log likelihood = {res}-1008.8935{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  knowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2}  .271798{col 26}{space 2}  .190997{col 37}{space 1}    1.42{col 46}{space 3}0.155{col 54}{space 4}-.1025492{col 67}{space 3} .6461452
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} .4151495{col 26}{space 2} .0498305{col 37}{space 1}    8.33{col 46}{space 3}0.000{col 54}{space 4} .3174836{col 67}{space 3} .5128154
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.994508{col 26}{space 2} .1986893{col 37}{space 1}   15.07{col 46}{space 3}0.000{col 54}{space 4} 2.605084{col 67}{space 3} 3.383932
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .2426577{col 44} .0825447{col 58}  .124578{col 70} .4726578
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.847769{col 44} .1137541{col 58} 1.637741{col 70} 2.084731
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}31.23{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.181
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-968.98076}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-968.98076}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       553
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}      11.1
{txt}{col 63}max{col 67}={col 69}{res}        25

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}    87.38
{txt}Log likelihood = {res}-968.98076{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .2656831{col 29}{space 2} .1893158{col 40}{space 1}    1.40{col 49}{space 3}0.161{col 57}{space 4} -.105369{col 70}{space 3} .6367353
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .4065495{col 29}{space 2} .0503686{col 40}{space 1}    8.07{col 49}{space 3}0.000{col 57}{space 4} .3078288{col 70}{space 3} .5052703
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1043076{col 29}{space 2} .0653552{col 40}{space 1}    1.60{col 49}{space 3}0.110{col 57}{space 4}-.0237863{col 70}{space 3} .2324015
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .4982357{col 29}{space 2} .3823558{col 40}{space 1}    1.30{col 49}{space 3}0.193{col 57}{space 4}-.2511678{col 70}{space 3} 1.247639
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8887252{col 29}{space 2} .4901824{col 40}{space 1}    1.81{col 49}{space 3}0.070{col 57}{space 4}-.0720148{col 70}{space 3} 1.849465
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .2025825{col 29}{space 2} .3043412{col 40}{space 1}    0.67{col 49}{space 3}0.506{col 57}{space 4}-.3939152{col 70}{space 3} .7990803
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0700923{col 29}{space 2} .3681577{col 40}{space 1}    0.19{col 49}{space 3}0.849{col 57}{space 4}-.6514834{col 70}{space 3} .7916681
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .4268821{col 29}{space 2} .4979731{col 40}{space 1}    0.86{col 49}{space 3}0.391{col 57}{space 4}-.5491274{col 70}{space 3} 1.402892
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .1669207{col 29}{space 2} .2979033{col 40}{space 1}    0.56{col 49}{space 3}0.575{col 57}{space 4} -.416959{col 70}{space 3} .7508003
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} -.198097{col 29}{space 2} .1253539{col 40}{space 1}   -1.58{col 49}{space 3}0.114{col 57}{space 4}-.4437862{col 70}{space 3} .0475921
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.531528{col 29}{space 2} .2828246{col 40}{space 1}    8.95{col 49}{space 3}0.000{col 57}{space 4} 1.977202{col 70}{space 3} 3.085854
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .2294926{col 44} .0804328{col 58} .1154619{col 70} .4561404
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.806548{col 44} .1133156{col 58} 1.597563{col 70} 2.042872
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}27.64{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.168
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 108.66475}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 108.66482}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 108.66482}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       957
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         9
{txt}{col 63}avg{col 67}={col 69}{res}      19.1
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.38
{txt}Log likelihood = {res} 108.66482{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.5398

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       talk3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0118681{col 26}{space 2} .0193573{col 37}{space 1}    0.61{col 46}{space 3}0.540{col 54}{space 4}-.0260715{col 67}{space 3} .0498077
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4372394{col 26}{space 2} .0145454{col 37}{space 1}   30.06{col 46}{space 3}0.000{col 54}{space 4} .4087309{col 67}{space 3}  .465748
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0021133{col 44} .0008914{col 58} .0009246{col 70} .0048305
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0451516{col 44} .0021158{col 58} .0411893{col 70} .0494949
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}13.06{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0002
{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.438
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 309.88178}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 310.33106}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:  310.3315}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:  310.3315}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       869
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      17.4
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   547.75
{txt}Log likelihood = {res}  310.3315{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       talk3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0067606{col 26}{space 2} .0116194{col 37}{space 1}    0.58{col 46}{space 3}0.561{col 54}{space 4} -.016013{col 67}{space 3} .0295342
{txt}{space 7}talk1 {c |}{col 14}{res}{space 2}  .649925{col 26}{space 2} .0277767{col 37}{space 1}   23.40{col 46}{space 3}0.000{col 54}{space 4} .5954836{col 67}{space 3} .7043664
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .171471{col 26}{space 2} .0146626{col 37}{space 1}   11.69{col 46}{space 3}0.000{col 54}{space 4} .1427329{col 67}{space 3} .2002091
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 2.39e-11{col 44} 1.39e-10{col 58} 2.58e-16{col 70} 2.20e-06
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0286643{col 44} .0013751{col 58} .0260919{col 70} .0314903
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}0.00{col 55}{txt}Prob >= chibar2 = {col 73}{res}1.0000
{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.446
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 309.29907}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 309.58374}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 309.58378}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 309.58378}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       839
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      16.8
{txt}{col 63}max{col 67}={col 69}{res}        27

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   547.33
{txt}Log likelihood = {res} 309.58378{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          talk3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0055469{col 29}{space 2} .0122008{col 40}{space 1}    0.45{col 49}{space 3}0.649{col 57}{space 4}-.0183662{col 70}{space 3} .0294599
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .6407344{col 29}{space 2} .0292937{col 40}{space 1}   21.87{col 49}{space 3}0.000{col 57}{space 4} .5833198{col 70}{space 3}  .698149
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0101977{col 29}{space 2} .0064687{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0024808{col 70}{space 3} .0228761
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0235966{col 29}{space 2} .0365251{col 40}{space 1}    0.65{col 49}{space 3}0.518{col 57}{space 4}-.0479913{col 70}{space 3} .0951845
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0648608{col 29}{space 2} .0430803{col 40}{space 1}    1.51{col 49}{space 3}0.132{col 57}{space 4}-.0195751{col 70}{space 3} .1492966
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0155361{col 29}{space 2} .0289173{col 40}{space 1}    0.54{col 49}{space 3}0.591{col 57}{space 4}-.0411407{col 70}{space 3} .0722129
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} -.017632{col 29}{space 2}  .035877{col 40}{space 1}   -0.49{col 49}{space 3}0.623{col 57}{space 4}-.0879497{col 70}{space 3} .0526856
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0443418{col 29}{space 2} .0486164{col 40}{space 1}   -0.91{col 49}{space 3}0.362{col 57}{space 4}-.1396282{col 70}{space 3} .0509447
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0005847{col 29}{space 2} .0289782{col 40}{space 1}   -0.02{col 49}{space 3}0.984{col 57}{space 4} -.057381{col 70}{space 3} .0562115
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0059275{col 29}{space 2}  .011689{col 40}{space 1}   -0.51{col 49}{space 3}0.612{col 57}{space 4}-.0288376{col 70}{space 3} .0169826
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  .143182{col 29}{space 2} .0239455{col 40}{space 1}    5.98{col 49}{space 3}0.000{col 57}{space 4} .0962498{col 70}{space 3} .1901143
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0001175{col 44} .0003825{col 58} 1.99e-07{col 70}  .069384
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0278781{col 44} .0014069{col 58} .0252527{col 70} .0307764
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}0.10{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.3742
{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.445
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\mixed2_2.tex", keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixed2_2.tex"'})

{com}.         eststo clear
{txt}
{com}.         
.         
.         
. // Reconstructing table A13 in appendix 8 //
. 
. // Multilevel random-intercept, and random-slopes models adjusted for baseline 
. // level of outcome & other control variables included.
. 
. // Directly after experiment
.         foreach k in interest values knowledge talk {c -(}
{txt}  2{com}.  eststo: xtmixed `k'2 t  `k'1 books i.mother i.father female  ||   teacherID : t  || classID : 
{txt}  3{com}.          gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 290.17904}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 291.64884}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:  291.6973}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 291.70301}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 291.70367}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res: 291.70373}  
{res}{txt}Iteration 6:{space 3}log likelihood = {res: 291.70373}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,092

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}       10{col 42}     30.3{col 53}       89
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        6{col 42}     18.5{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   794.25
{txt}Log likelihood = {res} 291.70373{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0139059{col 29}{space 2} .0121546{col 40}{space 1}   -1.14{col 49}{space 3}0.253{col 57}{space 4}-.0377285{col 70}{space 3} .0099166
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6619312{col 29}{space 2} .0242613{col 40}{space 1}   27.28{col 49}{space 3}0.000{col 57}{space 4}   .61438{col 70}{space 3} .7094824
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0173397{col 29}{space 2} .0062099{col 40}{space 1}    2.79{col 49}{space 3}0.005{col 57}{space 4} .0051685{col 70}{space 3}  .029511
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0183391{col 29}{space 2} .0344067{col 40}{space 1}    0.53{col 49}{space 3}0.594{col 57}{space 4}-.0490967{col 70}{space 3}  .085775
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0113837{col 29}{space 2} .0416205{col 40}{space 1}   -0.27{col 49}{space 3}0.784{col 57}{space 4}-.0929583{col 70}{space 3} .0701909
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} -.015239{col 29}{space 2} .0277013{col 40}{space 1}   -0.55{col 49}{space 3}0.582{col 57}{space 4}-.0695326{col 70}{space 3} .0390546
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0293056{col 29}{space 2} .0338661{col 40}{space 1}   -0.87{col 49}{space 3}0.387{col 57}{space 4}-.0956819{col 70}{space 3} .0370706
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0449751{col 29}{space 2} .0453821{col 40}{space 1}   -0.99{col 49}{space 3}0.322{col 57}{space 4}-.1339224{col 70}{space 3} .0439722
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0056919{col 29}{space 2} .0275292{col 40}{space 1}   -0.21{col 49}{space 3}0.836{col 57}{space 4}-.0596481{col 70}{space 3} .0482643
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0164616{col 29}{space 2} .0116138{col 40}{space 1}    1.42{col 49}{space 3}0.156{col 57}{space 4} -.006301{col 70}{space 3} .0392242
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1851304{col 29}{space 2} .0249564{col 40}{space 1}    7.42{col 49}{space 3}0.000{col 57}{space 4} .1362168{col 70}{space 3} .2340441
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 4.09e-10{col 44} 2.03e-09{col 58} 2.46e-14{col 70} 6.80e-06
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} .0233711{col 44} .0088113{col 58} .0111625{col 70} .0489323
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.53e-07{col 44} 4.10e-07{col 58} 8.03e-10{col 70} .0000292
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1840776{col 44} .0051245{col 58} .1743028{col 70} .1944005
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}3.10{col 59}{txt}Prob > chi2 ={col 73}{res}0.3771

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:interest2} {...}
{c |}{...}
{res}     0.633   523.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 1051.9539}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 1053.4965}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 1053.5344}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 1053.5345}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 1053.5345}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       994

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}        8{col 42}     27.6{col 53}       82
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        3{col 42}     16.8{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   588.02
{txt}Log likelihood = {res} 1053.5345{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        values2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0020524{col 29}{space 2} .0074732{col 40}{space 1}   -0.27{col 49}{space 3}0.784{col 57}{space 4}-.0166995{col 70}{space 3} .0125947
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5875667{col 29}{space 2} .0258672{col 40}{space 1}   22.71{col 49}{space 3}0.000{col 57}{space 4} .5368678{col 70}{space 3} .6382655
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0046527{col 29}{space 2} .0029507{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0011306{col 70}{space 3}  .010436
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0096862{col 29}{space 2} .0163349{col 40}{space 1}    0.59{col 49}{space 3}0.553{col 57}{space 4}-.0223296{col 70}{space 3} .0417021
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0128085{col 29}{space 2} .0201121{col 40}{space 1}    0.64{col 49}{space 3}0.524{col 57}{space 4}-.0266104{col 70}{space 3} .0522275
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0043489{col 29}{space 2} .0136025{col 40}{space 1}    0.32{col 49}{space 3}0.749{col 57}{space 4}-.0223115{col 70}{space 3} .0310093
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0136646{col 29}{space 2} .0162544{col 40}{space 1}   -0.84{col 49}{space 3}0.401{col 57}{space 4}-.0455226{col 70}{space 3} .0181934
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0560643{col 29}{space 2} .0217352{col 40}{space 1}   -2.58{col 49}{space 3}0.010{col 57}{space 4}-.0986644{col 70}{space 3}-.0134641
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0021671{col 29}{space 2} .0135713{col 40}{space 1}    0.16{col 49}{space 3}0.873{col 57}{space 4}-.0244321{col 70}{space 3} .0287663
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0117511{col 29}{space 2} .0056036{col 40}{space 1}    2.10{col 49}{space 3}0.036{col 57}{space 4} .0007683{col 70}{space 3}  .022734
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3118852{col 29}{space 2} .0216556{col 40}{space 1}   14.40{col 49}{space 3}0.000{col 57}{space 4}  .269441{col 70}{space 3} .3543294
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 9.62e-14{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 6.47e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .0195122{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .0822408{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}14.78{col 59}{txt}Prob > chi2 ={col 73}{res}0.0020

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:values2} {...}
{c |}{...}
{res}     0.792   471.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1098.8073}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1098.1235}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-1098.0525}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-1098.0489}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-1098.0488}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       661

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}        4{col 42}     18.4{col 53}       60
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        2{col 42}     11.2{col 53}       23
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   225.59
{txt}Log likelihood = {res}-1098.0488{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}  .131509{col 29}{space 2} .1281041{col 40}{space 1}    1.03{col 49}{space 3}0.305{col 57}{space 4}-.1195704{col 70}{space 3} .3825884
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .5676567{col 29}{space 2} .0431555{col 40}{space 1}   13.15{col 49}{space 3}0.000{col 57}{space 4} .4830735{col 70}{space 3} .6522399
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1799914{col 29}{space 2} .0537848{col 40}{space 1}    3.35{col 49}{space 3}0.001{col 57}{space 4} .0745751{col 70}{space 3} .2854076
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}  .021396{col 29}{space 2} .3139056{col 40}{space 1}    0.07{col 49}{space 3}0.946{col 57}{space 4}-.5938476{col 70}{space 3} .6366396
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0095788{col 29}{space 2} .4388329{col 40}{space 1}    0.02{col 49}{space 3}0.983{col 57}{space 4} -.850518{col 70}{space 3} .8696755
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.4363108{col 29}{space 2}  .260377{col 40}{space 1}   -1.68{col 49}{space 3}0.094{col 57}{space 4}-.9466404{col 70}{space 3} .0740189
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0984471{col 29}{space 2} .3025435{col 40}{space 1}    0.33{col 49}{space 3}0.745{col 57}{space 4}-.4945273{col 70}{space 3} .6914216
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .9278801{col 29}{space 2} .4423737{col 40}{space 1}    2.10{col 49}{space 3}0.036{col 57}{space 4} .0608436{col 70}{space 3} 1.794917
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .5882613{col 29}{space 2} .2503488{col 40}{space 1}    2.35{col 49}{space 3}0.019{col 57}{space 4} .0975866{col 70}{space 3} 1.078936
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3373081{col 29}{space 2} .1025672{col 40}{space 1}   -3.29{col 49}{space 3}0.001{col 57}{space 4} -.538336{col 70}{space 3}-.1362801
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.768318{col 29}{space 2} .2247806{col 40}{space 1}    7.87{col 49}{space 3}0.000{col 57}{space 4} 1.327756{col 70}{space 3}  2.20888
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} .2757138{col 44} .1340847{col 58} .1062927{col 70} .7151767
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} .3100968{col 44} .0804589{col 58} .1864839{col 70} .5156479
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.11e-06{col 44} 6.55e-06{col 58} 4.77e-09{col 70} .0009312
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.241208{col 44} .0352723{col 58} 1.173966{col 70} 1.312302
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}22.42{col 59}{txt}Prob > chi2 ={col 73}{res}0.0001

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:knowledge2} {...}
{c |}{...}
{res}     3.706   296.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 502.62463}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 504.77166}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 504.78677}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 504.78709}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 504.78709}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,061

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}       10{col 42}     29.5{col 53}       88
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        6{col 42}     18.0{col 53}       29
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}  1062.53
{txt}Log likelihood = {res} 504.78709{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          talk2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0090439{col 29}{space 2} .0096698{col 40}{space 1}    0.94{col 49}{space 3}0.350{col 57}{space 4}-.0099085{col 70}{space 3} .0279964
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .7092107{col 29}{space 2} .0226407{col 40}{space 1}   31.32{col 49}{space 3}0.000{col 57}{space 4} .6648358{col 70}{space 3} .7535857
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0068148{col 29}{space 2} .0051705{col 40}{space 1}    1.32{col 49}{space 3}0.187{col 57}{space 4}-.0033191{col 70}{space 3} .0169487
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0013931{col 29}{space 2} .0280304{col 40}{space 1}    0.05{col 49}{space 3}0.960{col 57}{space 4}-.0535454{col 70}{space 3} .0563316
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0145882{col 29}{space 2}  .034436{col 40}{space 1}   -0.42{col 49}{space 3}0.672{col 57}{space 4}-.0820816{col 70}{space 3} .0529051
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0130923{col 29}{space 2} .0225152{col 40}{space 1}   -0.58{col 49}{space 3}0.561{col 57}{space 4}-.0572212{col 70}{space 3} .0310367
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0040138{col 29}{space 2} .0275536{col 40}{space 1}   -0.15{col 49}{space 3}0.884{col 57}{space 4}-.0580179{col 70}{space 3} .0499903
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0240981{col 29}{space 2} .0384164{col 40}{space 1}   -0.63{col 49}{space 3}0.530{col 57}{space 4}-.0993929{col 70}{space 3} .0511968
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} -.000582{col 29}{space 2} .0223799{col 40}{space 1}   -0.03{col 49}{space 3}0.979{col 57}{space 4}-.0444458{col 70}{space 3} .0432818
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0123561{col 29}{space 2}   .00946{col 40}{space 1}    1.31{col 49}{space 3}0.192{col 57}{space 4}-.0061851{col 70}{space 3} .0308973
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1235968{col 29}{space 2} .0181985{col 40}{space 1}    6.79{col 49}{space 3}0.000{col 57}{space 4} .0879284{col 70}{space 3} .1592652
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 2.19e-09{col 44} 1.23e-08{col 58} 3.64e-14{col 70} .0001315
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .013712{col 44} .0086271{col 58} .0039954{col 70} .0470591
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 3.00e-11{col 44} 9.15e-11{col 58} 7.63e-14{col 70} 1.18e-08
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1498144{col 44} .0043483{col 58} .1415297{col 70}  .158584
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.87{col 59}{txt}Prob > chi2 ={col 73}{res}0.8323

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:talk2} {...}
{c |}{...}
{res}     0.423   510.000
{txt}{hline 13}{c BT}{hline 20}

{com}. esttab using "Tables\mixedB1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixedB1.tex"'})

{com}.         eststo clear
{txt}
{com}.         
. 
. // Reconstructing table A14 in appendix 8 //
. 
. // End of year
.         foreach k in interest values knowledge talk {c -(}
{txt}  2{com}.  eststo: xtmixed `k'3 t  `k'1 books i.mother i.father female  ||   teacherID : t  || classID : 
{txt}  3{com}.          gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  164.3245}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 165.65446}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 165.69489}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 165.71322}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 165.71332}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res: 165.71332}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       867

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     26.3{col 53}       76
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     17.3{col 53}       31
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   412.85
{txt}Log likelihood = {res} 165.71332{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0013634{col 29}{space 2} .0164861{col 40}{space 1}   -0.08{col 49}{space 3}0.934{col 57}{space 4}-.0336756{col 70}{space 3} .0309488
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .5867264{col 29}{space 2}  .030096{col 40}{space 1}   19.50{col 49}{space 3}0.000{col 57}{space 4} .5277393{col 70}{space 3} .6457136
{txt}{space 10}books {c |}{col 17}{res}{space 2}  .018337{col 29}{space 2} .0075663{col 40}{space 1}    2.42{col 49}{space 3}0.015{col 57}{space 4} .0035073{col 70}{space 3} .0331667
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0282321{col 29}{space 2} .0429316{col 40}{space 1}    0.66{col 49}{space 3}0.511{col 57}{space 4}-.0559124{col 70}{space 3} .1123765
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0870101{col 29}{space 2} .0503355{col 40}{space 1}    1.73{col 49}{space 3}0.084{col 57}{space 4}-.0116457{col 70}{space 3} .1856659
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0332382{col 29}{space 2}   .03371{col 40}{space 1}    0.99{col 49}{space 3}0.324{col 57}{space 4}-.0328322{col 70}{space 3} .0993087
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0179633{col 29}{space 2} .0422409{col 40}{space 1}   -0.43{col 49}{space 3}0.671{col 57}{space 4} -.100754{col 70}{space 3} .0648274
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0644962{col 29}{space 2} .0554987{col 40}{space 1}   -1.16{col 49}{space 3}0.245{col 57}{space 4}-.1732717{col 70}{space 3} .0442793
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0177965{col 29}{space 2} .0338107{col 40}{space 1}   -0.53{col 49}{space 3}0.599{col 57}{space 4}-.0840642{col 70}{space 3} .0484712
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0036069{col 29}{space 2}  .014066{col 40}{space 1}   -0.26{col 49}{space 3}0.798{col 57}{space 4}-.0311756{col 70}{space 3} .0239619
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1997576{col 29}{space 2}  .032396{col 40}{space 1}    6.17{col 49}{space 3}0.000{col 57}{space 4} .1362625{col 70}{space 3} .2632527
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 1.13e-11{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 6.06e-10{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .030459{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1979316{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}3.24{col 59}{txt}Prob > chi2 ={col 73}{res}0.3565

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:interest3} {...}
{c |}{...}
{res}     0.634   365.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 744.34812}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 745.49618}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 745.52826}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 745.52842}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 745.52842}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       783

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     23.7{col 53}       71
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     15.7{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   308.12
{txt}Log likelihood = {res} 745.52842{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        values3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0008509{col 29}{space 2} .0082467{col 40}{space 1}    0.10{col 49}{space 3}0.918{col 57}{space 4}-.0153124{col 70}{space 3} .0170142
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5221793{col 29}{space 2} .0336696{col 40}{space 1}   15.51{col 49}{space 3}0.000{col 57}{space 4} .4561881{col 70}{space 3} .5881705
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0119359{col 29}{space 2} .0037357{col 40}{space 1}    3.20{col 49}{space 3}0.001{col 57}{space 4}  .004614{col 70}{space 3} .0192578
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0147303{col 29}{space 2} .0206923{col 40}{space 1}    0.71{col 49}{space 3}0.477{col 57}{space 4}-.0258258{col 70}{space 3} .0552864
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0062643{col 29}{space 2} .0243287{col 40}{space 1}   -0.26{col 49}{space 3}0.797{col 57}{space 4}-.0539477{col 70}{space 3} .0414191
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0087416{col 29}{space 2} .0162165{col 40}{space 1}    0.54{col 49}{space 3}0.590{col 57}{space 4}-.0230421{col 70}{space 3} .0405253
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0022568{col 29}{space 2}  .020515{col 40}{space 1}   -0.11{col 49}{space 3}0.912{col 57}{space 4}-.0424656{col 70}{space 3} .0379519
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0119539{col 29}{space 2} .0275365{col 40}{space 1}    0.43{col 49}{space 3}0.664{col 57}{space 4}-.0420166{col 70}{space 3} .0659244
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}  .005724{col 29}{space 2} .0164124{col 40}{space 1}    0.35{col 49}{space 3}0.727{col 57}{space 4}-.0264437{col 70}{space 3} .0378917
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0226276{col 29}{space 2} .0069532{col 40}{space 1}    3.25{col 49}{space 3}0.001{col 57}{space 4} .0089996{col 70}{space 3} .0362556
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3453479{col 29}{space 2} .0282064{col 40}{space 1}   12.24{col 49}{space 3}0.000{col 57}{space 4} .2900643{col 70}{space 3} .4006314
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 7.90e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} .0100389{col 44} .0084873{col 58} .0019145{col 70} .0526411
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .013479{col 44} .0079428{col 58} .0042468{col 70} .0427806
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .0921808{col 44} .0024127{col 58} .0875713{col 70} .0970329
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}4.45{col 59}{txt}Prob > chi2 ={col 73}{res}0.2167

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:values3} {...}
{c |}{...}
{res}     0.810   328.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-968.55931}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-968.50535}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-968.50503}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-968.50503}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       553

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        1{col 42}     16.8{col 53}       52
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        1{col 42}     11.1{col 53}       25
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}    87.41
{txt}Log likelihood = {res}-968.50503{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .2826018{col 29}{space 2} .1785288{col 40}{space 1}    1.58{col 49}{space 3}0.113{col 57}{space 4}-.0673082{col 70}{space 3} .6325118
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .4036766{col 29}{space 2} .0503134{col 40}{space 1}    8.02{col 49}{space 3}0.000{col 57}{space 4} .3050643{col 70}{space 3}  .502289
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1052649{col 29}{space 2} .0652174{col 40}{space 1}    1.61{col 49}{space 3}0.107{col 57}{space 4}-.0225589{col 70}{space 3} .2330886
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .4817266{col 29}{space 2} .3822547{col 40}{space 1}    1.26{col 49}{space 3}0.208{col 57}{space 4}-.2674789{col 70}{space 3} 1.230932
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8987568{col 29}{space 2} .4901448{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0619094{col 70}{space 3} 1.859423
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .2049014{col 29}{space 2} .3044022{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.3917159{col 70}{space 3} .8015187
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0652724{col 29}{space 2} .3684727{col 40}{space 1}    0.18{col 49}{space 3}0.859{col 57}{space 4}-.6569209{col 70}{space 3} .7874656
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .4272137{col 29}{space 2} .4992294{col 40}{space 1}    0.86{col 49}{space 3}0.392{col 57}{space 4}-.5512579{col 70}{space 3} 1.405685
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .1621847{col 29}{space 2}  .298134{col 40}{space 1}    0.54{col 49}{space 3}0.586{col 57}{space 4}-.4221471{col 70}{space 3} .7465165
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.2086918{col 29}{space 2} .1250788{col 40}{space 1}   -1.67{col 49}{space 3}0.095{col 57}{space 4}-.4538418{col 70}{space 3} .0364582
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.536212{col 29}{space 2} .2803194{col 40}{space 1}    9.05{col 49}{space 3}0.000{col 57}{space 4} 1.986796{col 70}{space 3} 3.085628
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} .3259263{col 44}  .292045{col 58} .0562864{col 70} 1.887276
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} .2518745{col 44}  .275389{col 58} .0295472{col 70}   2.1471
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .3060557{col 44} .1937222{col 58} .0885158{col 70}  1.05823
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.345951{col 44} .0423828{col 58} 1.265393{col 70} 1.431637
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}28.59{col 59}{txt}Prob > chi2 ={col 73}{res}0.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:knowledge3} {...}
{c |}{...}
{res}     4.168   214.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 307.50063}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:  309.5659}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 309.58366}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 309.58378}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 309.58378}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       839

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     25.4{col 53}       75
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     16.8{col 53}       27
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}10{txt}){col 67}={col 70}{res}   547.33
{txt}Log likelihood = {res} 309.58378{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          talk3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0055469{col 29}{space 2} .0122008{col 40}{space 1}    0.45{col 49}{space 3}0.649{col 57}{space 4}-.0183662{col 70}{space 3} .0294599
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .6407344{col 29}{space 2} .0292937{col 40}{space 1}   21.87{col 49}{space 3}0.000{col 57}{space 4} .5833198{col 70}{space 3}  .698149
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0101977{col 29}{space 2} .0064687{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0024808{col 70}{space 3} .0228761
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0235966{col 29}{space 2} .0365251{col 40}{space 1}    0.65{col 49}{space 3}0.518{col 57}{space 4}-.0479913{col 70}{space 3} .0951845
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0648608{col 29}{space 2} .0430803{col 40}{space 1}    1.51{col 49}{space 3}0.132{col 57}{space 4}-.0195751{col 70}{space 3} .1492966
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0155361{col 29}{space 2} .0289173{col 40}{space 1}    0.54{col 49}{space 3}0.591{col 57}{space 4}-.0411407{col 70}{space 3} .0722129
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} -.017632{col 29}{space 2}  .035877{col 40}{space 1}   -0.49{col 49}{space 3}0.623{col 57}{space 4}-.0879497{col 70}{space 3} .0526856
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0443418{col 29}{space 2} .0486164{col 40}{space 1}   -0.91{col 49}{space 3}0.362{col 57}{space 4}-.1396282{col 70}{space 3} .0509447
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0005847{col 29}{space 2} .0289782{col 40}{space 1}   -0.02{col 49}{space 3}0.984{col 57}{space 4} -.057381{col 70}{space 3} .0562115
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0059275{col 29}{space 2}  .011689{col 40}{space 1}   -0.51{col 49}{space 3}0.612{col 57}{space 4}-.0288376{col 70}{space 3} .0169826
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  .143182{col 29}{space 2} .0239455{col 40}{space 1}    5.98{col 49}{space 3}0.000{col 57}{space 4} .0962498{col 70}{space 3} .1901143
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 4.49e-11{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.68e-10{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .0108392{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1669672{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.10{col 59}{txt}Prob > chi2 ={col 73}{res}0.9915

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:talk3} {...}
{c |}{...}
{res}     0.445   359.000
{txt}{hline 13}{c BT}{hline 20}

{com}. esttab using "Tables\mixedB2.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\mixedB2.tex"'})

{com}.         eststo clear
{txt}
{com}. 
.         
. // Reconstructing table A15 in appendix 9 //
. 
. // OLS models using alternative coding for knowledge
. // i) unadjusted, ii) adjusted for baseline level of outcome and
. // iii) adjusted for baseline level of outcome & other control variables included.
. 
. // Directly after experiment & end of year 
.         foreach k in xknowledge {c -(}
{txt}  2{com}.          eststo: reg `k'2 t   , vce(cl classID)
{txt}  3{com}.  eststo: reg `k'2 t  `k'1    , vce(cl classID)
{txt}  4{com}.  eststo: reg `k'2 t  `k'1 books i.mother i.father female , vce(cl classID)
{txt}  5{com}.          eststo: reg `k'3 t     , vce(cl classID)
{txt}  6{com}.  eststo: reg `k'3 t  `k'1   , vce(cl classID)
{txt}  7{com}.  eststo: reg `k'3 t  `k'1 books i.mother i.father female   , vce(cl classID)
{txt}  8{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}     1,213
                                                {txt}F(1, 58)          =  {res}     4.07
                                                {txt}Prob > F          = {res}    0.0483
                                                {txt}R-squared         = {res}    0.0117
                                                {txt}Root MSE          =    {res} 1.6199

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} xknowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3520326{col 26}{space 2} .1745428{col 37}{space 1}    2.02{col 46}{space 3}0.048{col 54}{space 4} .0026472{col 67}{space 3} .7014181
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.192708{col 26}{space 2} .1482403{col 37}{space 1}   21.54{col 46}{space 3}0.000{col 54}{space 4} 2.895973{col 67}{space 3} 3.489444
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

Linear regression                               Number of obs     = {res}     1,108
                                                {txt}F(2, 58)          =  {res}    89.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2481
                                                {txt}Root MSE          =    {res}  1.395

{txt}{ralign 78:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} xknowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0631239{col 26}{space 2} .1452892{col 37}{space 1}    0.43{col 46}{space 3}0.666{col 54}{space 4}-.2277041{col 67}{space 3} .3539518
{txt}{space 1}xknowledge1 {c |}{col 14}{res}{space 2} .6355418{col 26}{space 2} .0488864{col 37}{space 1}   13.00{col 46}{space 3}0.000{col 54}{space 4} .5376851{col 67}{space 3} .7333985
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.809093{col 26}{space 2} .1488425{col 37}{space 1}   12.15{col 46}{space 3}0.000{col 54}{space 4} 1.511153{col 67}{space 3} 2.107034
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

Linear regression                               Number of obs     = {res}     1,065
                                                {txt}F(10, 58)         =  {res}    26.42
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2817
                                                {txt}Root MSE          =    {res} 1.3658

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}    xknowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0890127{col 29}{space 2} .1380314{col 40}{space 1}    0.64{col 49}{space 3}0.522{col 57}{space 4}-.1872872{col 70}{space 3} .3653126
{txt}{space 4}xknowledge1 {c |}{col 17}{res}{space 2} .6224617{col 29}{space 2} .0462073{col 40}{space 1}   13.47{col 49}{space 3}0.000{col 57}{space 4} .5299677{col 70}{space 3} .7149558
{txt}{space 10}books {c |}{col 17}{res}{space 2}  .183497{col 29}{space 2} .0438466{col 40}{space 1}    4.18{col 49}{space 3}0.000{col 57}{space 4} .0957284{col 70}{space 3} .2712656
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .2539994{col 29}{space 2} .3061198{col 40}{space 1}    0.83{col 49}{space 3}0.410{col 57}{space 4} -.358766{col 70}{space 3} .8667647
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .2323586{col 29}{space 2} .2888249{col 40}{space 1}    0.80{col 49}{space 3}0.424{col 57}{space 4}-.3457872{col 70}{space 3} .8105045
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} -.121606{col 29}{space 2} .2507629{col 40}{space 1}   -0.48{col 49}{space 3}0.630{col 57}{space 4}-.6235625{col 70}{space 3} .3803506
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.1554308{col 29}{space 2} .3170936{col 40}{space 1}   -0.49{col 49}{space 3}0.626{col 57}{space 4}-.7901626{col 70}{space 3}  .479301
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .4151964{col 29}{space 2} .4301653{col 40}{space 1}    0.97{col 49}{space 3}0.338{col 57}{space 4} -.445873{col 70}{space 3} 1.276266
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}  .284549{col 29}{space 2} .2601171{col 40}{space 1}    1.09{col 49}{space 3}0.279{col 57}{space 4}-.2361318{col 70}{space 3} .8052299
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.4218213{col 29}{space 2} .0836276{col 40}{space 1}   -5.04{col 49}{space 3}0.000{col 57}{space 4}  -.58922{col 70}{space 3}-.2544225
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  1.48217{col 29}{space 2} .2343125{col 40}{space 1}    6.33{col 49}{space 3}0.000{col 57}{space 4} 1.013142{col 70}{space 3} 1.951197
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

Linear regression                               Number of obs     = {res}       977
                                                {txt}F(1, 49)          =  {res}     2.92
                                                {txt}Prob > F          = {res}    0.0936
                                                {txt}R-squared         = {res}    0.0128
                                                {txt}Root MSE          =    {res} 1.6522

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} xknowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3798047{col 26}{space 2} .2221043{col 37}{space 1}    1.71{col 46}{space 3}0.094{col 54}{space 4}-.0665306{col 67}{space 3} .8261401
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.827338{col 26}{space 2} .1614912{col 37}{space 1}   23.70{col 46}{space 3}0.000{col 54}{space 4} 3.502809{col 67}{space 3} 4.151867
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

Linear regression                               Number of obs     = {res}       886
                                                {txt}F(2, 49)          =  {res}    32.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1195
                                                {txt}Root MSE          =    {res} 1.5252

{txt}{ralign 78:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} xknowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .1715745{col 26}{space 2} .1989753{col 37}{space 1}    0.86{col 46}{space 3}0.393{col 54}{space 4}-.2282814{col 67}{space 3} .5714304
{txt}{space 1}xknowledge1 {c |}{col 14}{res}{space 2} .4351466{col 26}{space 2} .0561527{col 37}{space 1}    7.75{col 46}{space 3}0.000{col 54}{space 4} .3223035{col 67}{space 3} .5479898
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.909355{col 26}{space 2} .1793284{col 37}{space 1}   16.22{col 46}{space 3}0.000{col 54}{space 4} 2.548981{col 67}{space 3} 3.269729
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

Linear regression                               Number of obs     = {res}       854
                                                {txt}F(10, 49)         =  {res}     9.94
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1334
                                                {txt}Root MSE          =    {res} 1.5247

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}    xknowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .1582623{col 29}{space 2} .2003027{col 40}{space 1}    0.79{col 49}{space 3}0.433{col 57}{space 4}-.2442611{col 70}{space 3} .5607857
{txt}{space 4}xknowledge1 {c |}{col 17}{res}{space 2} .4337845{col 29}{space 2} .0566494{col 40}{space 1}    7.66{col 49}{space 3}0.000{col 57}{space 4} .3199433{col 70}{space 3} .5476257
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1014914{col 29}{space 2} .0674006{col 40}{space 1}    1.51{col 49}{space 3}0.139{col 57}{space 4}-.0339552{col 70}{space 3}  .236938
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .3737748{col 29}{space 2} .3160007{col 40}{space 1}    1.18{col 49}{space 3}0.243{col 57}{space 4}-.2612523{col 70}{space 3} 1.008802
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .5742818{col 29}{space 2}  .355921{col 40}{space 1}    1.61{col 49}{space 3}0.113{col 57}{space 4}-.1409682{col 70}{space 3} 1.289532
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .3162083{col 29}{space 2}  .261976{col 40}{space 1}    1.21{col 49}{space 3}0.233{col 57}{space 4}-.2102521{col 70}{space 3} .8426687
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0213728{col 29}{space 2} .3267846{col 40}{space 1}   -0.07{col 49}{space 3}0.948{col 57}{space 4} -.678071{col 70}{space 3} .6353254
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.2871154{col 29}{space 2} .4229354{col 40}{space 1}   -0.68{col 49}{space 3}0.500{col 57}{space 4}-1.137036{col 70}{space 3} .5628051
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}  .022489{col 29}{space 2} .2556833{col 40}{space 1}    0.09{col 49}{space 3}0.930{col 57}{space 4}-.4913258{col 70}{space 3} .5363038
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0583206{col 29}{space 2} .1337845{col 40}{space 1}   -0.44{col 49}{space 3}0.665{col 57}{space 4}-.3271707{col 70}{space 3} .2105295
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  2.41435{col 29}{space 2}  .261603{col 40}{space 1}    9.23{col 49}{space 3}0.000{col 57}{space 4} 1.888639{col 70}{space 3} 2.940061
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. esttab using "Tables\regxknowledge_.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\regxknowledge_.tex"'})

{com}.   eststo clear
{txt}
{com}.   
.   
. // Reconstructing table A16 in appendix 9 //
. 
. // Multilevel random-intercept model using alternative coding for knowledge
. // i) unadjusted, ii) adjusted for baseline level of outcome and
. // iii) adjusted for baseline level of outcome & other control variables included.
. 
. // Directly after experiment & end of year 
.   foreach k in xknowledge {c -(}
{txt}  2{com}.          eststo: xtmixed `k'2 t    || classID : , var
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: xtmixed `k'2 t  `k'1   || classID : , var
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: xtmixed `k'2 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k'2 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 13{com}. drop sample1`k'
{txt} 14{com}.          eststo: xtmixed `k'3 t    || classID : , var
{txt} 15{com}.                  gen sample1`k' = e(sample)
{txt} 16{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 17{com}. drop sample1`k'
{txt} 18{com}.  eststo: xtmixed `k'3 t  `k'1   || classID : , var
{txt} 19{com}.          gen sample1`k' = e(sample)
{txt} 20{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 21{com}. drop sample1`k'
{txt} 22{com}.  eststo: xtmixed `k'3 t  `k'1 books i.mother i.father female  || classID : , var
{txt} 23{com}.          gen sample1`k' = e(sample)
{txt} 24{com}. tabstat `k'3 if sample1`k'==1 & t==0, s(mean N) format(%10.3fc)
{txt} 25{com}. drop sample1`k'
{txt} 26{com}. {c )-}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2269.9567}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2269.9567}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,213
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         5
{txt}{col 63}avg{col 67}={col 69}{res}      20.6
{txt}{col 63}max{col 67}={col 69}{res}        33

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     6.35
{txt}Log likelihood = {res}-2269.9567{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0117

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} xknowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .4326596{col 26}{space 2} .1716973{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4}  .096139{col 67}{space 3} .7691801
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.123182{col 26}{space 2} .1230091{col 37}{space 1}   25.39{col 46}{space 3}0.000{col 54}{space 4} 2.882089{col 67}{space 3} 3.364276
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3088501{col 44} .0803978{col 58} .1854252{col 70} .5144307
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.322193{col 44} .0966877{col 58} 2.140214{col 70} 2.519644
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}70.58{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge2} {...}
{c |}{...}
{res}     3.193   576.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1915.1169}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1915.1169}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,108
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         4
{txt}{col 63}avg{col 67}={col 69}{res}      18.8
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   352.01
{txt}Log likelihood = {res}-1915.1169{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} xknowledge2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .1365523{col 26}{space 2} .1447782{col 37}{space 1}    0.94{col 46}{space 3}0.346{col 54}{space 4}-.1472078{col 67}{space 3} .4203125
{txt}{space 1}xknowledge1 {c |}{col 14}{res}{space 2} .6258878{col 26}{space 2} .0336646{col 37}{space 1}   18.59{col 46}{space 3}0.000{col 54}{space 4} .5599064{col 67}{space 3} .6918692
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.792805{col 26}{space 2} .1283188{col 37}{space 1}   13.97{col 46}{space 3}0.000{col 54}{space 4} 1.541304{col 67}{space 3} 2.044305
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .2021842{col 44} .0574248{col 58} .1158743{col 70} .3527828
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.749837{col 44} .0764653{col 58} 1.606206{col 70} 1.906311
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}48.78{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge2} {...}
{c |}{...}
{res}     3.261   514.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1816.5711}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1816.5711}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,065
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        59

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         4
{txt}{col 63}avg{col 67}={col 69}{res}      18.1
{txt}{col 63}max{col 67}={col 69}{res}        30

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   395.09
{txt}Log likelihood = {res}-1816.5711{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    xknowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .1645638{col 29}{space 2} .1427755{col 40}{space 1}    1.15{col 49}{space 3}0.249{col 57}{space 4}-.1152709{col 70}{space 3} .4443986
{txt}{space 4}xknowledge1 {c |}{col 17}{res}{space 2} .6123508{col 29}{space 2} .0340817{col 40}{space 1}   17.97{col 49}{space 3}0.000{col 57}{space 4} .5455519{col 70}{space 3} .6791497
{txt}{space 10}books {c |}{col 17}{res}{space 2}   .15093{col 29}{space 2} .0454911{col 40}{space 1}    3.32{col 49}{space 3}0.001{col 57}{space 4}  .061769{col 70}{space 3}  .240091
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}  .343108{col 29}{space 2}  .246686{col 40}{space 1}    1.39{col 49}{space 3}0.164{col 57}{space 4}-.1403878{col 70}{space 3} .8266037
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .3365672{col 29}{space 2} .2995783{col 40}{space 1}    1.12{col 49}{space 3}0.261{col 57}{space 4}-.2505955{col 70}{space 3} .9237299
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0103419{col 29}{space 2}  .203326{col 40}{space 1}   -0.05{col 49}{space 3}0.959{col 57}{space 4}-.4088535{col 70}{space 3} .3881697
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.1381493{col 29}{space 2} .2432166{col 40}{space 1}   -0.57{col 49}{space 3}0.570{col 57}{space 4}-.6148451{col 70}{space 3} .3385465
{txt}Nordic Country  {c |}{col 17}{res}{space 2}  .497277{col 29}{space 2} .3303523{col 40}{space 1}    1.51{col 49}{space 3}0.132{col 57}{space 4}-.1502015{col 70}{space 3} 1.144756
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .3430048{col 29}{space 2} .2008547{col 40}{space 1}    1.71{col 49}{space 3}0.088{col 57}{space 4}-.0506632{col 70}{space 3} .7366729
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3831332{col 29}{space 2} .0870344{col 40}{space 1}   -4.40{col 49}{space 3}0.000{col 57}{space 4}-.5537175{col 70}{space 3}-.2125489
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.389208{col 29}{space 2}  .180711{col 40}{space 1}    7.69{col 49}{space 3}0.000{col 57}{space 4} 1.035021{col 70}{space 3} 1.743395
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .1939904{col 44} .0568628{col 58}  .109213{col 70} .3445768
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.669978{col 44}  .074616{col 58} 1.529954{col 70} 1.822817
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}42.13{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge2} {...}
{c |}{...}
{res}     3.219   498.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1828.5136}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1828.5136}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       977
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         9
{txt}{col 63}avg{col 67}={col 69}{res}      19.5
{txt}{col 63}max{col 67}={col 69}{res}        32

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     3.12
{txt}Log likelihood = {res}-1828.5136{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0772

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} xknowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .3795201{col 26}{space 2} .2147713{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0414239{col 67}{space 3} .8004641
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.774976{col 26}{space 2}  .161123{col 37}{space 1}   23.43{col 46}{space 3}0.000{col 54}{space 4} 3.459181{col 67}{space 3} 4.090772
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .4381729{col 44} .1137662{col 58} .2634156{col 70} .7288691
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.287595{col 44} .1062616{col 58} 2.088525{col 70} 2.505639
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}94.70{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge3} {...}
{c |}{...}
{res}     3.827   417.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1592.3029}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1592.3029}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       886
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      17.7
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}2{txt}){col 67}={col 70}{res}   106.38
{txt}Log likelihood = {res}-1592.3029{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} xknowledge3{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .1711712{col 26}{space 2} .1981728{col 37}{space 1}    0.86{col 46}{space 3}0.388{col 54}{space 4}-.2172403{col 67}{space 3} .5595828
{txt}{space 1}xknowledge1 {c |}{col 14}{res}{space 2} .4100375{col 26}{space 2} .0403126{col 37}{space 1}   10.17{col 46}{space 3}0.000{col 54}{space 4} .3310262{col 67}{space 3} .4890487
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.945848{col 26}{space 2} .1753461{col 37}{space 1}   16.80{col 46}{space 3}0.000{col 54}{space 4} 2.602176{col 67}{space 3}  3.28952
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3500571{col 44} .0957275{col 58} .2048176{col 70} .5982884
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.971668{col 44} .0964367{col 58} 1.791433{col 70} 2.170037
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}74.74{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge3} {...}
{c |}{...}
{res}     3.938   369.000
{txt}{hline 13}{c BT}{hline 20}
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1530.2104}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1530.2104}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       854
{txt}Group variable: {res}classID{col 49}{txt}Number of groups{col 67}={col 69}{res}        50

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      17.1
{txt}{col 63}max{col 67}={col 69}{res}        31

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}   120.00
{txt}Log likelihood = {res}-1530.2104{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    xknowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .1533698{col 29}{space 2} .2025909{col 40}{space 1}    0.76{col 49}{space 3}0.449{col 57}{space 4} -.243701{col 70}{space 3} .5504405
{txt}{space 4}xknowledge1 {c |}{col 17}{res}{space 2} .4159219{col 29}{space 2} .0413583{col 40}{space 1}   10.06{col 49}{space 3}0.000{col 57}{space 4} .3348612{col 70}{space 3} .4969827
{txt}{space 10}books {c |}{col 17}{res}{space 2}  .045228{col 29}{space 2}  .055307{col 40}{space 1}    0.82{col 49}{space 3}0.413{col 57}{space 4}-.0631717{col 70}{space 3} .1536277
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .5449845{col 29}{space 2} .3089202{col 40}{space 1}    1.76{col 49}{space 3}0.078{col 57}{space 4}-.0604879{col 70}{space 3} 1.150457
{txt}Nordic Country  {c |}{col 17}{res}{space 2}  .638947{col 29}{space 2} .3623828{col 40}{space 1}    1.76{col 49}{space 3}0.078{col 57}{space 4}-.0713103{col 70}{space 3} 1.349204
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .3605475{col 29}{space 2} .2462421{col 40}{space 1}    1.46{col 49}{space 3}0.143{col 57}{space 4}-.1220781{col 70}{space 3} .8431731
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0592313{col 29}{space 2} .3031076{col 40}{space 1}   -0.20{col 49}{space 3}0.845{col 57}{space 4}-.6533113{col 70}{space 3} .5348487
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0150134{col 29}{space 2} .3989712{col 40}{space 1}    0.04{col 49}{space 3}0.970{col 57}{space 4}-.7669559{col 70}{space 3} .7969826
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0362711{col 29}{space 2} .2459295{col 40}{space 1}    0.15{col 49}{space 3}0.883{col 57}{space 4}-.4457419{col 70}{space 3} .5182841
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.1855421{col 29}{space 2} .1066039{col 40}{space 1}   -1.74{col 49}{space 3}0.082{col 57}{space 4}-.3944818{col 70}{space 3} .0233977
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.573102{col 29}{space 2} .2443405{col 40}{space 1}   10.53{col 49}{space 3}0.000{col 57}{space 4} 2.094203{col 70}{space 3}    3.052
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3678833{col 44} .1011929{col 58} .2145714{col 70}  .630737
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 1.942279{col 44} .0969616{col 58} 1.761239{col 70} 2.141928
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}72.44{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:xknowledge3} {...}
{c |}{...}
{res}     3.930   359.000
{txt}{hline 13}{c BT}{hline 20}

{com}. esttab using "Tables\xknowledge_.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>        eqlabels("" "sd(Intercept at level 2)" "sd(Residuals at level 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"Tables\xknowledge_.tex"'})

{com}.   eststo clear
{txt}
{com}.  
.  
. // Reconstructing table A17 in appendix 10 //
. 
. // Generate strata indicator for teachers participating with more then 1 class.
. qui by teacherID classID , sort: generate nvals = _n == 1
{txt}
{com}. qui by teacherID:  replace nvals = sum(nvals)
{txt}
{com}. qui by teacherID: replace nvals = nvals[_N]
{txt}
{com}. gen strata = teacherID if nvals!=1
{txt}(607 missing values generated)

{com}. replace strata = 99 if nvals==1
{txt}(607 real changes made)

{com}. 
. 
. // Calculate p-values for permutation tests adjusted for baseline level of outcome & other
. // control variables included.
. 
. // Directly after experiment
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}. ritest t _b[t], reps(2000) cluster(classID) strata(strata) seed(123): reg `k'2 t  `k'1  books i.mother i.father female    , vce(cl classID)     
{txt}  3{com}. {c )-}
{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}     1,092
                                                {txt}F(10, 58)         =  {res}    50.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4266
                                                {txt}Root MSE          =    {res} .18645

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0121023{col 29}{space 2} .0124055{col 40}{space 1}   -0.98{col 49}{space 3}0.333{col 57}{space 4}-.0369347{col 70}{space 3} .0127301
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6647927{col 29}{space 2} .0323032{col 40}{space 1}   20.58{col 49}{space 3}0.000{col 57}{space 4} .6001308{col 70}{space 3} .7294546
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0176045{col 29}{space 2} .0073662{col 40}{space 1}    2.39{col 49}{space 3}0.020{col 57}{space 4} .0028595{col 70}{space 3} .0323495
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0178028{col 29}{space 2} .0346679{col 40}{space 1}    0.51{col 49}{space 3}0.610{col 57}{space 4}-.0515926{col 70}{space 3} .0871982
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0149713{col 29}{space 2} .0371149{col 40}{space 1}   -0.40{col 49}{space 3}0.688{col 57}{space 4}-.0892649{col 70}{space 3} .0593223
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0166893{col 29}{space 2} .0246419{col 40}{space 1}   -0.68{col 49}{space 3}0.501{col 57}{space 4}-.0660155{col 70}{space 3} .0326369
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0329415{col 29}{space 2} .0344341{col 40}{space 1}   -0.96{col 49}{space 3}0.343{col 57}{space 4}-.1018688{col 70}{space 3} .0359858
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0476514{col 29}{space 2} .0505185{col 40}{space 1}   -0.94{col 49}{space 3}0.349{col 57}{space 4} -.148775{col 70}{space 3} .0534723
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0077942{col 29}{space 2} .0230439{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.0539216{col 70}{space 3} .0383333
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0145644{col 29}{space 2}  .011191{col 40}{space 1}    1.30{col 49}{space 3}0.198{col 57}{space 4}-.0078367{col 70}{space 3} .0369656
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1858754{col 29}{space 2} .0303479{col 40}{space 1}    6.12{col 49}{space 3}0.000{col 57}{space 4} .1251274{col 70}{space 3} .2466233
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress interest2 t interest1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.0121023{col 27}    692{col 35}   2000{col 43} 0.3460{col 51} 0.0106{col 59} .3251407{col 69}{space 1} .3673117
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       994
                                                {txt}F(10, 58)         =  {res}    57.27
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3878
                                                {txt}Root MSE          =    {res} .08494

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0034781{col 29}{space 2}  .007598{col 40}{space 1}   -0.46{col 49}{space 3}0.649{col 57}{space 4}-.0186871{col 70}{space 3} .0117309
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5991808{col 29}{space 2} .0276316{col 40}{space 1}   21.68{col 49}{space 3}0.000{col 57}{space 4} .5438702{col 70}{space 3} .6544913
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0045791{col 29}{space 2} .0037551{col 40}{space 1}    1.22{col 49}{space 3}0.228{col 57}{space 4}-.0029376{col 70}{space 3} .0120958
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0073874{col 29}{space 2} .0147186{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4} -.022075{col 70}{space 3} .0368498
{txt}Nordic Country  {c |}{col 17}{res}{space 2}  .010555{col 29}{space 2} .0169585{col 40}{space 1}    0.62{col 49}{space 3}0.536{col 57}{space 4}-.0233912{col 70}{space 3} .0445012
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0040722{col 29}{space 2}  .014613{col 40}{space 1}    0.28{col 49}{space 3}0.781{col 57}{space 4}-.0251789{col 70}{space 3} .0333233
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} -.014346{col 29}{space 2} .0140731{col 40}{space 1}   -1.02{col 49}{space 3}0.312{col 57}{space 4}-.0425163{col 70}{space 3} .0138244
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0567321{col 29}{space 2} .0228625{col 40}{space 1}   -2.48{col 49}{space 3}0.016{col 57}{space 4}-.1024964{col 70}{space 3}-.0109678
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0004789{col 29}{space 2} .0131712{col 40}{space 1}    0.04{col 49}{space 3}0.971{col 57}{space 4}-.0258862{col 70}{space 3} .0268439
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0125909{col 29}{space 2} .0059574{col 40}{space 1}    2.11{col 49}{space 3}0.039{col 57}{space 4} .0006658{col 70}{space 3} .0245159
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3056007{col 29}{space 2} .0267243{col 40}{space 1}   11.44{col 49}{space 3}0.000{col 57}{space 4} .2521062{col 70}{space 3} .3590951
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress values2 t values1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.0034781{col 27}   1249{col 35}   2000{col 43} 0.6245{col 51} 0.0108{col 59}  .602857{col 69}{space 1} .6457773
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       661
                                                {txt}F(10, 58)         =  {res}    13.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2565
                                                {txt}Root MSE          =    {res} 1.3068

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0681706{col 29}{space 2} .1430078{col 40}{space 1}    0.48{col 49}{space 3}0.635{col 57}{space 4}-.2180906{col 70}{space 3} .3544318
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2}   .56781{col 29}{space 2}  .060483{col 40}{space 1}    9.39{col 49}{space 3}0.000{col 57}{space 4} .4467402{col 70}{space 3} .6888799
{txt}{space 10}books {c |}{col 17}{res}{space 2} .2248919{col 29}{space 2} .0606245{col 40}{space 1}    3.71{col 49}{space 3}0.000{col 57}{space 4} .1035387{col 70}{space 3} .3462451
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.1212849{col 29}{space 2} .3582094{col 40}{space 1}   -0.34{col 49}{space 3}0.736{col 57}{space 4}-.8383188{col 70}{space 3} .5957491
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.1072696{col 29}{space 2} .2920684{col 40}{space 1}   -0.37{col 49}{space 3}0.715{col 57}{space 4} -.691908{col 70}{space 3} .4773688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.5182216{col 29}{space 2} .2978497{col 40}{space 1}   -1.74{col 49}{space 3}0.087{col 57}{space 4}-1.114433{col 70}{space 3} .0779894
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0811357{col 29}{space 2} .3667154{col 40}{space 1}    0.22{col 49}{space 3}0.826{col 57}{space 4}-.6529248{col 70}{space 3} .8151963
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .7500018{col 29}{space 2} .5458411{col 40}{space 1}    1.37{col 49}{space 3}0.175{col 57}{space 4}-.3426179{col 70}{space 3} 1.842621
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .4818495{col 29}{space 2} .3153484{col 40}{space 1}    1.53{col 49}{space 3}0.132{col 57}{space 4} -.149389{col 70}{space 3} 1.113088
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.3696383{col 29}{space 2} .1051654{col 40}{space 1}   -3.51{col 49}{space 3}0.001{col 57}{space 4}-.5801497{col 70}{space 3}-.1591268
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.932468{col 29}{space 2} .2818377{col 40}{space 1}    6.86{col 49}{space 3}0.000{col 57}{space 4} 1.368308{col 70}{space 3} 2.496627
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress knowledge2 t knowledge1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .0681706{col 27}   1207{col 35}   2000{col 43} 0.6035{col 51} 0.0109{col 59} .5816726{col 69}{space 1} .6250235
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}     1,061
                                                {txt}F(10, 58)         =  {res}    88.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5059
                                                {txt}Root MSE          =    {res} .15121

{txt}{ralign 81:(Std. Err. adjusted for {res:59} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0088686{col 29}{space 2}  .009053{col 40}{space 1}    0.98{col 49}{space 3}0.331{col 57}{space 4} -.009253{col 70}{space 3} .0269902
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2} .7120993{col 29}{space 2} .0259006{col 40}{space 1}   27.49{col 49}{space 3}0.000{col 57}{space 4} .6602536{col 70}{space 3}  .763945
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0072404{col 29}{space 2} .0044375{col 40}{space 1}    1.63{col 49}{space 3}0.108{col 57}{space 4}-.0016423{col 70}{space 3} .0161231
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0001952{col 29}{space 2}  .028212{col 40}{space 1}    0.01{col 49}{space 3}0.995{col 57}{space 4}-.0562772{col 70}{space 3} .0566676
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0172613{col 29}{space 2} .0319376{col 40}{space 1}   -0.54{col 49}{space 3}0.591{col 57}{space 4}-.0811913{col 70}{space 3} .0466688
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0147605{col 29}{space 2} .0207593{col 40}{space 1}   -0.71{col 49}{space 3}0.480{col 57}{space 4}-.0563147{col 70}{space 3} .0267938
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0056186{col 29}{space 2} .0298155{col 40}{space 1}   -0.19{col 49}{space 3}0.851{col 57}{space 4}-.0653007{col 70}{space 3} .0540635
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0256548{col 29}{space 2} .0388453{col 40}{space 1}   -0.66{col 49}{space 3}0.512{col 57}{space 4}-.1034121{col 70}{space 3} .0521025
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0016652{col 29}{space 2} .0210454{col 40}{space 1}   -0.08{col 49}{space 3}0.937{col 57}{space 4}-.0437922{col 70}{space 3} .0404618
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}  .011868{col 29}{space 2}  .010195{col 40}{space 1}    1.16{col 49}{space 3}0.249{col 57}{space 4}-.0085395{col 70}{space 3} .0322754
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1237926{col 29}{space 2} .0191645{col 40}{space 1}    6.46{col 49}{space 3}0.000{col 57}{space 4} .0854306{col 70}{space 3} .1621546
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress talk2 t talk1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .0088686{col 27}    560{col 35}   2000{col 43} 0.2800{col 51} 0.0100{col 59} .2604063{col 69}{space 1} .3002406
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. 
. // End of year
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}. ritest t _b[t], reps(2000) cluster(classID) strata(strata) seed(123): reg `k'3 t  `k'1  books i.mother i.father female    , vce(cl classID)     
{txt}  3{com}. {c )-}
{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       867
                                                {txt}F(10, 49)         =  {res}    42.39
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3274
                                                {txt}Root MSE          =    {res} .20153

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0017879{col 29}{space 2} .0172916{col 40}{space 1}   -0.10{col 49}{space 3}0.918{col 57}{space 4}-.0365366{col 70}{space 3} .0329608
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .5905666{col 29}{space 2} .0323022{col 40}{space 1}   18.28{col 49}{space 3}0.000{col 57}{space 4} .5256529{col 70}{space 3} .6554804
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0196272{col 29}{space 2} .0092138{col 40}{space 1}    2.13{col 49}{space 3}0.038{col 57}{space 4} .0011114{col 70}{space 3}  .038143
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0268165{col 29}{space 2} .0499759{col 40}{space 1}    0.54{col 49}{space 3}0.594{col 57}{space 4}-.0736139{col 70}{space 3} .1272469
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0822787{col 29}{space 2}  .059805{col 40}{space 1}    1.38{col 49}{space 3}0.175{col 57}{space 4}-.0379039{col 70}{space 3} .2024614
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0318299{col 29}{space 2} .0433314{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.0552477{col 70}{space 3} .1189075
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0148803{col 29}{space 2}  .046758{col 40}{space 1}   -0.32{col 49}{space 3}0.752{col 57}{space 4} -.108844{col 70}{space 3} .0790834
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0590968{col 29}{space 2} .0645294{col 40}{space 1}   -0.92{col 49}{space 3}0.364{col 57}{space 4}-.1887734{col 70}{space 3} .0705798
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0149128{col 29}{space 2} .0396585{col 40}{space 1}   -0.38{col 49}{space 3}0.709{col 57}{space 4}-.0946095{col 70}{space 3} .0647839
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0014458{col 29}{space 2} .0131652{col 40}{space 1}   -0.11{col 49}{space 3}0.913{col 57}{space 4}-.0279023{col 70}{space 3} .0250108
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1933031{col 29}{space 2} .0340532{col 40}{space 1}    5.68{col 49}{space 3}0.000{col 57}{space 4} .1248708{col 70}{space 3} .2617355
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress interest3 t interest1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.0017879{col 27}   1838{col 35}   2000{col 43} 0.9190{col 51} 0.0061{col 59} .9061686{col 69}{space 1} .9305863
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       783
                                                {txt}F(10, 49)         =  {res}    39.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2999
                                                {txt}Root MSE          =    {res} .09431

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        values3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0002943{col 29}{space 2} .0087908{col 40}{space 1}   -0.03{col 49}{space 3}0.973{col 57}{space 4}-.0179602{col 70}{space 3} .0173715
{txt}{space 8}values1 {c |}{col 17}{res}{space 2} .5332502{col 29}{space 2} .0317772{col 40}{space 1}   16.78{col 49}{space 3}0.000{col 57}{space 4} .4693916{col 70}{space 3} .5971089
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0120606{col 29}{space 2} .0039864{col 40}{space 1}    3.03{col 49}{space 3}0.004{col 57}{space 4} .0040497{col 70}{space 3} .0200716
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0159259{col 29}{space 2} .0209714{col 40}{space 1}    0.76{col 49}{space 3}0.451{col 57}{space 4}-.0262177{col 70}{space 3} .0580696
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0034884{col 29}{space 2}  .017992{col 40}{space 1}   -0.19{col 49}{space 3}0.847{col 57}{space 4}-.0396447{col 70}{space 3} .0326679
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0115944{col 29}{space 2}  .015434{col 40}{space 1}    0.75{col 49}{space 3}0.456{col 57}{space 4}-.0194215{col 70}{space 3} .0426102
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0046553{col 29}{space 2} .0248911{col 40}{space 1}   -0.19{col 49}{space 3}0.852{col 57}{space 4}-.0546759{col 70}{space 3} .0453653
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0065758{col 29}{space 2} .0298807{col 40}{space 1}    0.22{col 49}{space 3}0.827{col 57}{space 4}-.0534717{col 70}{space 3} .0666232
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0049987{col 29}{space 2} .0185346{col 40}{space 1}    0.27{col 49}{space 3}0.789{col 57}{space 4} -.032248{col 70}{space 3} .0422454
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} .0237157{col 29}{space 2} .0068329{col 40}{space 1}    3.47{col 49}{space 3}0.001{col 57}{space 4} .0099845{col 70}{space 3} .0374469
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3358117{col 29}{space 2} .0303269{col 40}{space 1}   11.07{col 49}{space 3}0.000{col 57}{space 4} .2748674{col 70}{space 3} .3967559
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress values3 t values1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.0002943{col 27}   1936{col 35}   2000{col 43} 0.9680{col 51} 0.0039{col 59} .9593187{col 69}{space 1} .9752707
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       553
                                                {txt}F(10, 49)         =  {res}     8.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1503
                                                {txt}Root MSE          =    {res} 1.4453

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     knowledge3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .3070968{col 29}{space 2} .2013972{col 40}{space 1}    1.52{col 49}{space 3}0.134{col 57}{space 4}-.0976261{col 70}{space 3} .7118196
{txt}{space 5}knowledge1 {c |}{col 17}{res}{space 2} .4250509{col 29}{space 2} .0677561{col 40}{space 1}    6.27{col 49}{space 3}0.000{col 57}{space 4} .2888899{col 70}{space 3} .5612119
{txt}{space 10}books {c |}{col 17}{res}{space 2} .1368964{col 29}{space 2} .0792457{col 40}{space 1}    1.73{col 49}{space 3}0.090{col 57}{space 4}-.0223537{col 70}{space 3} .2961466
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .4298643{col 29}{space 2}  .422204{col 40}{space 1}    1.02{col 49}{space 3}0.314{col 57}{space 4}-.4185865{col 70}{space 3} 1.278315
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .8181836{col 29}{space 2} .3903759{col 40}{space 1}    2.10{col 49}{space 3}0.041{col 57}{space 4} .0336938{col 70}{space 3} 1.602673
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .1422207{col 29}{space 2} .2824547{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.4253934{col 70}{space 3} .7098347
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .1109432{col 29}{space 2} .3758281{col 40}{space 1}    0.30{col 49}{space 3}0.769{col 57}{space 4}-.6443117{col 70}{space 3}  .866198
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .2766683{col 29}{space 2} .5857656{col 40}{space 1}    0.47{col 49}{space 3}0.639{col 57}{space 4}-.9004718{col 70}{space 3} 1.453808
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .2182893{col 29}{space 2} .2422651{col 40}{space 1}    0.90{col 49}{space 3}0.372{col 57}{space 4}-.2685607{col 70}{space 3} .7051394
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2} -.154347{col 29}{space 2} .1290919{col 40}{space 1}   -1.20{col 49}{space 3}0.238{col 57}{space 4}-.4137668{col 70}{space 3} .1050728
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.408984{col 29}{space 2} .3098663{col 40}{space 1}    7.77{col 49}{space 3}0.000{col 57}{space 4} 1.786284{col 70}{space 3} 3.031683
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress knowledge3 t knowledge1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .3070967{col 27}    305{col 35}   2000{col 43} 0.1525{col 51} 0.0080{col 59} .1370124{col 69}{space 1} .1690136
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}(running regress on estimation sample)

Linear regression                               Number of obs     = {res}       839
                                                {txt}F(10, 49)         =  {res}    62.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3965
                                                {txt}Root MSE          =    {res} .16842

{txt}{ralign 81:(Std. Err. adjusted for {res:50} clusters in classID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          talk3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0056919{col 29}{space 2} .0129306{col 40}{space 1}    0.44{col 49}{space 3}0.662{col 57}{space 4} -.020293{col 70}{space 3} .0316769
{txt}{space 10}talk1 {c |}{col 17}{res}{space 2}  .641795{col 29}{space 2} .0281394{col 40}{space 1}   22.81{col 49}{space 3}0.000{col 57}{space 4} .5852467{col 70}{space 3} .6983433
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0102984{col 29}{space 2} .0048732{col 40}{space 1}    2.11{col 49}{space 3}0.040{col 57}{space 4} .0005054{col 70}{space 3} .0200914
{txt}{space 15} {c |}
{space 9}mother {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2} .0230584{col 29}{space 2} .0456547{col 40}{space 1}    0.51{col 49}{space 3}0.616{col 57}{space 4}-.0686882{col 70}{space 3}  .114805
{txt}Nordic Country  {c |}{col 17}{res}{space 2} .0636618{col 29}{space 2} .0415008{col 40}{space 1}    1.53{col 49}{space 3}0.131{col 57}{space 4}-.0197372{col 70}{space 3} .1470608
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2} .0148773{col 29}{space 2} .0310287{col 40}{space 1}    0.48{col 49}{space 3}0.634{col 57}{space 4}-.0474771{col 70}{space 3} .0772318
{txt}{space 15} {c |}
{space 9}father {c |}
{space 8}Europe  {c |}{col 17}{res}{space 2}-.0178378{col 29}{space 2} .0452468{col 40}{space 1}   -0.39{col 49}{space 3}0.695{col 57}{space 4}-.1087647{col 70}{space 3} .0730891
{txt}Nordic Country  {c |}{col 17}{res}{space 2}-.0441212{col 29}{space 2}  .051209{col 40}{space 1}   -0.86{col 49}{space 3}0.393{col 57}{space 4}-.1470295{col 70}{space 3} .0587871
{txt}{space 8}Sweden  {c |}{col 17}{res}{space 2}-.0004367{col 29}{space 2}  .030916{col 40}{space 1}   -0.01{col 49}{space 3}0.989{col 57}{space 4}-.0625647{col 70}{space 3} .0616913
{txt}{space 15} {c |}
{space 9}female {c |}{col 17}{res}{space 2}-.0056101{col 29}{space 2} .0126993{col 40}{space 1}   -0.44{col 49}{space 3}0.661{col 57}{space 4}-.0311303{col 70}{space 3}   .01991
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .1427326{col 29}{space 2} .0256349{col 40}{space 1}    5.57{col 49}{space 3}0.000{col 57}{space 4} .0912173{col 70}{space 3} .1942479
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Resampling replications ({res}2000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
..................................................  1050
..................................................  1100
..................................................  1150
..................................................  1200
..................................................  1250
..................................................  1300
..................................................  1350
..................................................  1400
..................................................  1450
..................................................  1500
..................................................  1550
..................................................  1600
..................................................  1650
..................................................  1700
..................................................  1750
..................................................  1800
..................................................  1850
..................................................  1900
..................................................  1950
..................................................  2000
{p2colset 7 17 21 2}{...}

{p2col :command:}regress talk3 t talk1 books i.mother i.father female, vce(cl classID){p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[t]}{p_end}
  res. var(s):  t
   Resampling:  Permuting t
Clust. var(s){res}:  classID
     {txt}Clusters{res}:  59
{txt}Strata var(s){res}:  strata
       {txt}Strata{res}:  15

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .0056919{col 27}   1300{col 35}   2000{col 43} 0.6500{col 51} 0.0107{col 59} .6286383{col 69}{space 1} .6709211
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. 
. 
. // Reconstructing table A18 in appendix 11 //
. 
. // Generate strata indicators for teachers participating with more then 1 class.
. qui tab teacherID if nvals>1, gen(blocks)  
{txt}
{com}. foreach var of varlist blocks1-blocks14     {c -(}
{txt}  2{com}. replace `var' = 0 if `var' ==.
{txt}  3{com}. {c )-}
{txt}(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)
(607 real changes made)

{com}. gen blocks0= 1 if nvals<2
{txt}(832 missing values generated)

{com}. replace blocks0=0 if nvals>1
{txt}(832 real changes made)

{com}. 
. 
. // Saturated regression models using method from Lin (2013). All coavariates, including
. // strata indicators are centered around their respective grand mean. The centered coaviates
. // are included in the regressions and also interacted with the treatment indicator. 
. // Standard error clustered at class level. See appendix 11 for a more detailed discussion. 
. 
. // Directly after experiment
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}.         local Z  female father2-father4 mother2-mother4 books blocks1-blocks14
{txt}  3{com}. quietly xtmixed `k'2 t `k'1 `Z'   ||   teacherID : t  || classID :
{txt}  4{com}. gen sample1`k' = e(sample)
{txt}  5{com}. foreach var in `k'1 female father1 father2 father3 father4 mother1 mother2 mother3 mother4 books1 books2 books3 books4 blocks1 blocks2 blocks3 blocks4 blocks5 blocks6 blocks7 blocks8 blocks9 blocks10 blocks11 blocks12 blocks13 blocks14 books {c -(}
{txt}  6{com}. qui noisily capture drop `var'me 
{txt}  7{com}. qui noisily capture drop `var'ce
{txt}  8{com}. egen `var'me = mean(cond(sample1`k'==1, `var', .))
{txt}  9{com}. gen `var'ce=`var'-`var'me
{txt} 10{com}. {c )-}
{txt} 11{com}. local X  female father2-father4 mother2-mother4 books blocks1-blocks14
{txt} 12{com}. eststo: xtmixed `k'2 t `k'1 `X' i.t#c.`k'1ce i.t#c.femalece  i.t#c.father2ce i.t#c.father3ce i.t#c.father4ce  i.t#c.mother2ce i.t#c.mother3ce i.t#c.mother4ce i.t#c.blocks1ce   i.t#c.blocks2ce i.t#c.blocks3ce i.t#c.blocks4ce i.t#c.blocks5ce i.t#c.blocks6ce i.t#c.blocks7ce i.t#c.blocks8ce i.t#c.blocks9ce i.t#c.blocks10ce        i.t#c.blocks11ce        i.t#c.blocks12ce        i.t#c.blocks13ce        i.t#c.blocks14ce i.t#c.booksce   ||   teacherID : t  || classID :
{txt} 13{com}. qui noisily capture drop sample1`k'
{txt} 14{com}. {c )-}
{txt}(131 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: 1.t#c.interest1ce omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks9ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 315.36233}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 318.44426}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 319.87099}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:  319.9195}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:  319.9195}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,092

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}       10{col 42}     30.3{col 53}       89
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        6{col 42}     18.5{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}46{txt}){col 67}={col 70}{res}   919.07
{txt}Log likelihood = {res}  319.9195{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2}-.0151632{col 29}{space 2} .0117648{col 40}{space 1}   -1.29{col 49}{space 3}0.197{col 57}{space 4}-.0382217{col 70}{space 3} .0078954
{txt}{space 6}interest1 {c |}{col 17}{res}{space 2} .6644044{col 29}{space 2} .0343342{col 40}{space 1}   19.35{col 49}{space 3}0.000{col 57}{space 4} .5971107{col 70}{space 3} .7316981
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0233507{col 29}{space 2} .0165107{col 40}{space 1}    1.41{col 49}{space 3}0.157{col 57}{space 4}-.0090097{col 70}{space 3} .0557112
{txt}{space 8}father2 {c |}{col 17}{res}{space 2}-.0192554{col 29}{space 2} .0443922{col 40}{space 1}   -0.43{col 49}{space 3}0.664{col 57}{space 4}-.1062625{col 70}{space 3} .0677518
{txt}{space 8}father3 {c |}{col 17}{res}{space 2}-.0545999{col 29}{space 2} .0592212{col 40}{space 1}   -0.92{col 49}{space 3}0.357{col 57}{space 4}-.1706712{col 70}{space 3} .0614715
{txt}{space 8}father4 {c |}{col 17}{res}{space 2} .0077064{col 29}{space 2} .0363002{col 40}{space 1}    0.21{col 49}{space 3}0.832{col 57}{space 4}-.0634407{col 70}{space 3} .0788535
{txt}{space 8}mother2 {c |}{col 17}{res}{space 2} .0249837{col 29}{space 2} .0463561{col 40}{space 1}    0.54{col 49}{space 3}0.590{col 57}{space 4}-.0658725{col 70}{space 3}   .11584
{txt}{space 8}mother3 {c |}{col 17}{res}{space 2}-.0160048{col 29}{space 2} .0574638{col 40}{space 1}   -0.28{col 49}{space 3}0.781{col 57}{space 4}-.1286319{col 70}{space 3} .0966222
{txt}{space 8}mother4 {c |}{col 17}{res}{space 2} -.031575{col 29}{space 2} .0369174{col 40}{space 1}   -0.86{col 49}{space 3}0.392{col 57}{space 4}-.1039317{col 70}{space 3} .0407818
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0317011{col 29}{space 2} .0085185{col 40}{space 1}    3.72{col 49}{space 3}0.000{col 57}{space 4} .0150051{col 70}{space 3} .0483972
{txt}{space 8}blocks1 {c |}{col 17}{res}{space 2}-.0580012{col 29}{space 2} .0422421{col 40}{space 1}   -1.37{col 49}{space 3}0.170{col 57}{space 4}-.1407941{col 70}{space 3} .0247917
{txt}{space 8}blocks2 {c |}{col 17}{res}{space 2}-.0460424{col 29}{space 2} .0403259{col 40}{space 1}   -1.14{col 49}{space 3}0.254{col 57}{space 4}-.1250798{col 70}{space 3}  .032995
{txt}{space 8}blocks3 {c |}{col 17}{res}{space 2}-.0770758{col 29}{space 2} .0354383{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1465335{col 70}{space 3} -.007618
{txt}{space 8}blocks4 {c |}{col 17}{res}{space 2} .0216705{col 29}{space 2}   .04064{col 40}{space 1}    0.53{col 49}{space 3}0.594{col 57}{space 4}-.0579825{col 70}{space 3} .1013234
{txt}{space 8}blocks5 {c |}{col 17}{res}{space 2} .0072589{col 29}{space 2} .0391144{col 40}{space 1}    0.19{col 49}{space 3}0.853{col 57}{space 4}-.0694039{col 70}{space 3} .0839218
{txt}{space 8}blocks6 {c |}{col 17}{res}{space 2} .0280787{col 29}{space 2} .0539197{col 40}{space 1}    0.52{col 49}{space 3}0.603{col 57}{space 4} -.077602{col 70}{space 3} .1337593
{txt}{space 8}blocks7 {c |}{col 17}{res}{space 2}-.0254179{col 29}{space 2} .0409071{col 40}{space 1}   -0.62{col 49}{space 3}0.534{col 57}{space 4}-.1055943{col 70}{space 3} .0547585
{txt}{space 8}blocks8 {c |}{col 17}{res}{space 2} .0706344{col 29}{space 2} .0425248{col 40}{space 1}    1.66{col 49}{space 3}0.097{col 57}{space 4}-.0127126{col 70}{space 3} .1539815
{txt}{space 8}blocks9 {c |}{col 17}{res}{space 2}-.1667308{col 29}{space 2} .0751416{col 40}{space 1}   -2.22{col 49}{space 3}0.026{col 57}{space 4}-.3140056{col 70}{space 3}-.0194561
{txt}{space 7}blocks10 {c |}{col 17}{res}{space 2}-.0094514{col 29}{space 2}  .037439{col 40}{space 1}   -0.25{col 49}{space 3}0.801{col 57}{space 4}-.0828306{col 70}{space 3} .0639277
{txt}{space 7}blocks11 {c |}{col 17}{res}{space 2} .0679251{col 29}{space 2} .0519544{col 40}{space 1}    1.31{col 49}{space 3}0.191{col 57}{space 4}-.0339037{col 70}{space 3} .1697539
{txt}{space 7}blocks12 {c |}{col 17}{res}{space 2}-.0182324{col 29}{space 2} .0307253{col 40}{space 1}   -0.59{col 49}{space 3}0.553{col 57}{space 4}-.0784529{col 70}{space 3} .0419881
{txt}{space 7}blocks13 {c |}{col 17}{res}{space 2}-.0661025{col 29}{space 2} .0353313{col 40}{space 1}   -1.87{col 49}{space 3}0.061{col 57}{space 4}-.1353505{col 70}{space 3} .0031455
{txt}{space 7}blocks14 {c |}{col 17}{res}{space 2}-.0561686{col 29}{space 2} .0374434{col 40}{space 1}   -1.50{col 49}{space 3}0.134{col 57}{space 4}-.1295563{col 70}{space 3} .0172191
{txt}{space 15} {c |}
t#c.interest1ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0205042{col 29}{space 2}  .048657{col 40}{space 1}    0.42{col 49}{space 3}0.673{col 57}{space 4}-.0748618{col 70}{space 3} .1158702
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 3}t#c.femalece {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0125271{col 29}{space 2} .0244672{col 40}{space 1}   -0.51{col 49}{space 3}0.609{col 57}{space 4}-.0604818{col 70}{space 3} .0354277
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0054167{col 29}{space 2} .0681898{col 40}{space 1}   -0.08{col 49}{space 3}0.937{col 57}{space 4}-.1390663{col 70}{space 3} .1282329
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0706837{col 29}{space 2} .0928277{col 40}{space 1}    0.76{col 49}{space 3}0.446{col 57}{space 4}-.1112551{col 70}{space 3} .2526226
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} -.019192{col 29}{space 2}  .055494{col 40}{space 1}   -0.35{col 49}{space 3}0.729{col 57}{space 4}-.1279582{col 70}{space 3} .0895742
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} -.004404{col 29}{space 2} .0692393{col 40}{space 1}   -0.06{col 49}{space 3}0.949{col 57}{space 4}-.1401105{col 70}{space 3} .1313025
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0207851{col 29}{space 2}   .08372{col 40}{space 1}    0.25{col 49}{space 3}0.804{col 57}{space 4} -.143303{col 70}{space 3} .1848732
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0382257{col 29}{space 2} .0565381{col 40}{space 1}    0.68{col 49}{space 3}0.499{col 57}{space 4}-.0725871{col 70}{space 3} .1490384
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks1ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0941197{col 29}{space 2} .0609537{col 40}{space 1}    1.54{col 49}{space 3}0.123{col 57}{space 4}-.0253475{col 70}{space 3} .2135868
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .1168134{col 29}{space 2} .0750544{col 40}{space 1}    1.56{col 49}{space 3}0.120{col 57}{space 4}-.0302904{col 70}{space 3} .2639173
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0071337{col 29}{space 2} .0488385{col 40}{space 1}   -0.15{col 49}{space 3}0.884{col 57}{space 4}-.1028554{col 70}{space 3}  .088588
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks5ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0332421{col 29}{space 2} .0675552{col 40}{space 1}    0.49{col 49}{space 3}0.623{col 57}{space 4}-.0991636{col 70}{space 3} .1656477
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks6ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0817836{col 29}{space 2} .0644575{col 40}{space 1}   -1.27{col 49}{space 3}0.205{col 57}{space 4} -.208118{col 70}{space 3} .0445509
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks7ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0257614{col 29}{space 2} .0581122{col 40}{space 1}   -0.44{col 49}{space 3}0.658{col 57}{space 4}-.1396592{col 70}{space 3} .0881364
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks8ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .1348216{col 29}{space 2} .0701409{col 40}{space 1}    1.92{col 49}{space 3}0.055{col 57}{space 4}-.0026521{col 70}{space 3} .2722952
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks9ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .2016071{col 29}{space 2} .0848878{col 40}{space 1}    2.37{col 49}{space 3}0.018{col 57}{space 4}   .03523{col 70}{space 3} .3679842
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks10ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0645173{col 29}{space 2} .0522779{col 40}{space 1}    1.23{col 49}{space 3}0.217{col 57}{space 4}-.0379455{col 70}{space 3} .1669802
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks11ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0037431{col 29}{space 2} .0644288{col 40}{space 1}   -0.06{col 49}{space 3}0.954{col 57}{space 4}-.1300213{col 70}{space 3} .1225351
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks12ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0102526{col 29}{space 2} .0506302{col 40}{space 1}   -0.20{col 49}{space 3}0.840{col 57}{space 4}-.1094859{col 70}{space 3} .0889807
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks13ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0340665{col 29}{space 2} .0622974{col 40}{space 1}    0.55{col 49}{space 3}0.584{col 57}{space 4}-.0880341{col 70}{space 3} .1561672
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks14ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0604885{col 29}{space 2} .0536826{col 40}{space 1}    1.13{col 49}{space 3}0.260{col 57}{space 4}-.0447275{col 70}{space 3} .1657044
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 4}t#c.booksce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0266562{col 29}{space 2} .0125538{col 40}{space 1}   -2.12{col 49}{space 3}0.034{col 57}{space 4}-.0512612{col 70}{space 3}-.0020512
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .1599402{col 29}{space 2} .0375921{col 40}{space 1}    4.25{col 49}{space 3}0.000{col 57}{space 4}  .086261{col 70}{space 3} .2336194
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 1.12e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.39e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.82e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1805216{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.00{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est1{txt} stored)
(204 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: 1.t#c.values1ce omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks9ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 1071.6779}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 1073.5665}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:  1074.401}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 1074.4358}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 1074.4358}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       994

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}        8{col 42}     27.6{col 53}       82
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        3{col 42}     16.8{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}46{txt}){col 67}={col 70}{res}   698.64
{txt}Log likelihood = {res} 1074.4358{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       values2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}t {c |}{col 16}{res}{space 2}-.0013191{col 28}{space 2}    .0061{col 39}{space 1}   -0.22{col 48}{space 3}0.829{col 56}{space 4} -.013275{col 69}{space 3} .0106368
{txt}{space 7}values1 {c |}{col 16}{res}{space 2} .5660851{col 28}{space 2} .0353512{col 39}{space 1}   16.01{col 48}{space 3}0.000{col 56}{space 4} .4967981{col 69}{space 3} .6353722
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0102967{col 28}{space 2} .0078474{col 39}{space 1}    1.31{col 48}{space 3}0.189{col 56}{space 4}-.0050839{col 69}{space 3} .0256772
{txt}{space 7}father2 {c |}{col 16}{res}{space 2}-.0212438{col 28}{space 2}  .021139{col 39}{space 1}   -1.00{col 48}{space 3}0.315{col 56}{space 4}-.0626755{col 69}{space 3} .0201878
{txt}{space 7}father3 {c |}{col 16}{res}{space 2}-.0569854{col 28}{space 2} .0287437{col 39}{space 1}   -1.98{col 48}{space 3}0.047{col 56}{space 4}-.1133221{col 69}{space 3}-.0006487
{txt}{space 7}father4 {c |}{col 16}{res}{space 2} .0094201{col 28}{space 2}  .017362{col 39}{space 1}    0.54{col 48}{space 3}0.587{col 56}{space 4}-.0246087{col 69}{space 3} .0434489
{txt}{space 7}mother2 {c |}{col 16}{res}{space 2} .0435139{col 28}{space 2} .0221224{col 39}{space 1}    1.97{col 48}{space 3}0.049{col 56}{space 4} .0001547{col 69}{space 3}  .086873
{txt}{space 7}mother3 {c |}{col 16}{res}{space 2} .0520403{col 28}{space 2} .0267818{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.0004511{col 69}{space 3} .1045316
{txt}{space 7}mother4 {c |}{col 16}{res}{space 2} .0178244{col 28}{space 2} .0177331{col 39}{space 1}    1.01{col 48}{space 3}0.315{col 56}{space 4} -.016932{col 69}{space 3} .0525807
{txt}{space 9}books {c |}{col 16}{res}{space 2} .0085682{col 28}{space 2} .0040289{col 39}{space 1}    2.13{col 48}{space 3}0.033{col 56}{space 4} .0006717{col 69}{space 3} .0164647
{txt}{space 7}blocks1 {c |}{col 16}{res}{space 2}-.0050435{col 28}{space 2} .0220159{col 39}{space 1}   -0.23{col 48}{space 3}0.819{col 56}{space 4}-.0481939{col 69}{space 3} .0381069
{txt}{space 7}blocks2 {c |}{col 16}{res}{space 2} .0020125{col 28}{space 2}  .021095{col 39}{space 1}    0.10{col 48}{space 3}0.924{col 56}{space 4}-.0393328{col 69}{space 3} .0433579
{txt}{space 7}blocks3 {c |}{col 16}{res}{space 2}-.0157968{col 28}{space 2} .0186416{col 39}{space 1}   -0.85{col 48}{space 3}0.397{col 56}{space 4}-.0523337{col 69}{space 3}   .02074
{txt}{space 7}blocks4 {c |}{col 16}{res}{space 2} .0031097{col 28}{space 2} .0195847{col 39}{space 1}    0.16{col 48}{space 3}0.874{col 56}{space 4}-.0352756{col 69}{space 3}  .041495
{txt}{space 7}blocks5 {c |}{col 16}{res}{space 2} -.005097{col 28}{space 2} .0193329{col 39}{space 1}   -0.26{col 48}{space 3}0.792{col 56}{space 4}-.0429886{col 69}{space 3} .0327947
{txt}{space 7}blocks6 {c |}{col 16}{res}{space 2}-.0236524{col 28}{space 2} .0270575{col 39}{space 1}   -0.87{col 48}{space 3}0.382{col 56}{space 4}-.0766842{col 69}{space 3} .0293794
{txt}{space 7}blocks7 {c |}{col 16}{res}{space 2} -.037183{col 28}{space 2} .0213671{col 39}{space 1}   -1.74{col 48}{space 3}0.082{col 56}{space 4}-.0790617{col 69}{space 3} .0046958
{txt}{space 7}blocks8 {c |}{col 16}{res}{space 2}-.0050206{col 28}{space 2} .0211269{col 39}{space 1}   -0.24{col 48}{space 3}0.812{col 56}{space 4}-.0464285{col 69}{space 3} .0363874
{txt}{space 7}blocks9 {c |}{col 16}{res}{space 2}-.0766968{col 28}{space 2} .0352398{col 39}{space 1}   -2.18{col 48}{space 3}0.030{col 56}{space 4}-.1457655{col 69}{space 3} -.007628
{txt}{space 6}blocks10 {c |}{col 16}{res}{space 2}-.0053205{col 28}{space 2} .0195259{col 39}{space 1}   -0.27{col 48}{space 3}0.785{col 56}{space 4}-.0435906{col 69}{space 3} .0329495
{txt}{space 6}blocks11 {c |}{col 16}{res}{space 2}   .08493{col 28}{space 2} .0260706{col 39}{space 1}    3.26{col 48}{space 3}0.001{col 56}{space 4} .0338326{col 69}{space 3} .1360275
{txt}{space 6}blocks12 {c |}{col 16}{res}{space 2}-.0143396{col 28}{space 2} .0171196{col 39}{space 1}   -0.84{col 48}{space 3}0.402{col 56}{space 4}-.0478935{col 69}{space 3} .0192142
{txt}{space 6}blocks13 {c |}{col 16}{res}{space 2}-.0254137{col 28}{space 2} .0183929{col 39}{space 1}   -1.38{col 48}{space 3}0.167{col 56}{space 4}-.0614631{col 69}{space 3} .0106358
{txt}{space 6}blocks14 {c |}{col 16}{res}{space 2}-.0204447{col 28}{space 2} .0194469{col 39}{space 1}   -1.05{col 48}{space 3}0.293{col 56}{space 4}-.0585598{col 69}{space 3} .0176705
{txt}{space 14} {c |}
{space 1}t#c.values1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0418229{col 28}{space 2} .0522436{col 39}{space 1}    0.80{col 48}{space 3}0.423{col 56}{space 4}-.0605728{col 69}{space 3} .1442186
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 2}t#c.femalece {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .005122{col 28}{space 2} .0116909{col 39}{space 1}    0.44{col 48}{space 3}0.661{col 56}{space 4}-.0177918{col 69}{space 3} .0280357
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0248281{col 28}{space 2} .0331551{col 39}{space 1}    0.75{col 48}{space 3}0.454{col 56}{space 4}-.0401546{col 69}{space 3} .0898108
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0058936{col 28}{space 2} .0445489{col 39}{space 1}    0.13{col 48}{space 3}0.895{col 56}{space 4}-.0814206{col 69}{space 3} .0932077
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0155533{col 28}{space 2} .0279694{col 39}{space 1}   -0.56{col 48}{space 3}0.578{col 56}{space 4}-.0703723{col 69}{space 3} .0392658
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0602364{col 28}{space 2} .0333502{col 39}{space 1}   -1.81{col 48}{space 3}0.071{col 56}{space 4}-.1256017{col 69}{space 3} .0051288
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0739154{col 28}{space 2} .0410726{col 39}{space 1}   -1.80{col 48}{space 3}0.072{col 56}{space 4}-.1544162{col 69}{space 3} .0065853
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0138996{col 28}{space 2} .0282428{col 39}{space 1}   -0.49{col 48}{space 3}0.623{col 56}{space 4}-.0692545{col 69}{space 3} .0414553
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .012012{col 28}{space 2} .0331805{col 39}{space 1}    0.36{col 48}{space 3}0.717{col 56}{space 4}-.0530207{col 69}{space 3} .0770446
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0311706{col 28}{space 2} .0364889{col 39}{space 1}    0.85{col 48}{space 3}0.393{col 56}{space 4}-.0403464{col 69}{space 3} .1026876
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0232038{col 28}{space 2} .0242333{col 39}{space 1}    0.96{col 48}{space 3}0.338{col 56}{space 4}-.0242926{col 69}{space 3} .0707003
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks5ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0428979{col 28}{space 2} .0345926{col 39}{space 1}    1.24{col 48}{space 3}0.215{col 56}{space 4}-.0249024{col 69}{space 3} .1106981
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks6ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0474305{col 28}{space 2} .0323985{col 39}{space 1}    1.46{col 48}{space 3}0.143{col 56}{space 4}-.0160695{col 69}{space 3} .1109304
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks7ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0394668{col 28}{space 2}   .03106{col 39}{space 1}    1.27{col 48}{space 3}0.204{col 56}{space 4}-.0214098{col 69}{space 3} .1003434
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks8ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0234875{col 28}{space 2} .0356109{col 39}{space 1}    0.66{col 48}{space 3}0.510{col 56}{space 4}-.0463086{col 69}{space 3} .0932835
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks9ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0793116{col 28}{space 2} .0409112{col 39}{space 1}    1.94{col 48}{space 3}0.053{col 56}{space 4}-.0008728{col 69}{space 3} .1594961
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks10ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0542116{col 28}{space 2} .0272205{col 39}{space 1}    1.99{col 48}{space 3}0.046{col 56}{space 4} .0008605{col 69}{space 3} .1075627
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks11ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0578046{col 28}{space 2} .0322843{col 39}{space 1}   -1.79{col 48}{space 3}0.073{col 56}{space 4}-.1210807{col 69}{space 3} .0054716
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks12ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0473971{col 28}{space 2} .0274521{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0064081{col 69}{space 3} .1012022
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks13ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0278954{col 28}{space 2} .0331467{col 39}{space 1}    0.84{col 48}{space 3}0.400{col 56}{space 4}-.0370709{col 69}{space 3} .0928617
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks14ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0470766{col 28}{space 2} .0276727{col 39}{space 1}    1.70{col 48}{space 3}0.089{col 56}{space 4} -.007161{col 69}{space 3} .1013142
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 3}t#c.booksce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0104783{col 28}{space 2}  .005967{col 39}{space 1}   -1.76{col 48}{space 3}0.079{col 56}{space 4}-.0221733{col 69}{space 3} .0012168
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .3069157{col 28}{space 2} .0309038{col 39}{space 1}    9.93{col 48}{space 3}0.000{col 56}{space 4} .2463454{col 69}{space 3}  .367486
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 7.11e-10{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.78e-09{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .008578{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .0816867{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.82{col 59}{txt}Prob > chi2 ={col 73}{res}0.8458

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est2{txt} stored)
(516 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: 1.t#c.knowledge1ce omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks9ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1073.9473}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1071.1454}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-1071.0753}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-1071.0718}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-1071.0718}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       661

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}        4{col 42}     18.4{col 53}       60
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        2{col 42}     11.2{col 53}       23
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}46{txt}){col 67}={col 70}{res}   336.86
{txt}Log likelihood = {res}-1071.0718{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      knowledge2{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}t {c |}{col 18}{res}{space 2} .1808315{col 30}{space 2} .1069518{col 41}{space 1}    1.69{col 50}{space 3}0.091{col 58}{space 4}-.0287901{col 71}{space 3} .3904532
{txt}{space 6}knowledge1 {c |}{col 18}{res}{space 2} .5107497{col 30}{space 2} .0579835{col 41}{space 1}    8.81{col 50}{space 3}0.000{col 58}{space 4} .3971042{col 71}{space 3} .6243953
{txt}{space 10}female {c |}{col 18}{res}{space 2} -.423536{col 30}{space 2} .1373337{col 41}{space 1}   -3.08{col 50}{space 3}0.002{col 58}{space 4}-.6927052{col 71}{space 3}-.1543668
{txt}{space 9}father2 {c |}{col 18}{res}{space 2} .3774437{col 30}{space 2} .3616728{col 41}{space 1}    1.04{col 50}{space 3}0.297{col 58}{space 4} -.331422{col 71}{space 3} 1.086309
{txt}{space 9}father3 {c |}{col 18}{res}{space 2} .7385739{col 30}{space 2} .5408371{col 41}{space 1}    1.37{col 50}{space 3}0.172{col 58}{space 4}-.3214473{col 71}{space 3} 1.798595
{txt}{space 9}father4 {c |}{col 18}{res}{space 2} .6245193{col 30}{space 2} .2983661{col 41}{space 1}    2.09{col 50}{space 3}0.036{col 58}{space 4} .0397326{col 71}{space 3} 1.209306
{txt}{space 9}mother2 {c |}{col 18}{res}{space 2}-.1746698{col 30}{space 2} .3929829{col 41}{space 1}   -0.44{col 50}{space 3}0.657{col 58}{space 4}-.9449021{col 71}{space 3} .5955625
{txt}{space 9}mother3 {c |}{col 18}{res}{space 2} .0228415{col 30}{space 2} .5658463{col 41}{space 1}    0.04{col 50}{space 3}0.968{col 58}{space 4}-1.086197{col 71}{space 3}  1.13188
{txt}{space 9}mother4 {c |}{col 18}{res}{space 2}-.2882218{col 30}{space 2} .3121562{col 41}{space 1}   -0.92{col 50}{space 3}0.356{col 58}{space 4}-.9000366{col 71}{space 3}  .323593
{txt}{space 11}books {c |}{col 18}{res}{space 2} .1525127{col 30}{space 2} .0720328{col 41}{space 1}    2.12{col 50}{space 3}0.034{col 58}{space 4}  .011331{col 71}{space 3} .2936943
{txt}{space 9}blocks1 {c |}{col 18}{res}{space 2} .4439677{col 30}{space 2} .3775835{col 41}{space 1}    1.18{col 50}{space 3}0.240{col 58}{space 4}-.2960823{col 71}{space 3} 1.184018
{txt}{space 9}blocks2 {c |}{col 18}{res}{space 2} .9151112{col 30}{space 2} .4014149{col 41}{space 1}    2.28{col 50}{space 3}0.023{col 58}{space 4} .1283525{col 71}{space 3}  1.70187
{txt}{space 9}blocks3 {c |}{col 18}{res}{space 2}-.2565766{col 30}{space 2} .2867607{col 41}{space 1}   -0.89{col 50}{space 3}0.371{col 58}{space 4}-.8186172{col 71}{space 3}  .305464
{txt}{space 9}blocks4 {c |}{col 18}{res}{space 2} .2489074{col 30}{space 2} .3343855{col 41}{space 1}    0.74{col 50}{space 3}0.457{col 58}{space 4}-.4064762{col 71}{space 3} .9042909
{txt}{space 9}blocks5 {c |}{col 18}{res}{space 2} 1.003007{col 30}{space 2} .3323892{col 41}{space 1}    3.02{col 50}{space 3}0.003{col 58}{space 4}  .351536{col 71}{space 3} 1.654478
{txt}{space 9}blocks6 {c |}{col 18}{res}{space 2} .4938303{col 30}{space 2} .3883439{col 41}{space 1}    1.27{col 50}{space 3}0.204{col 58}{space 4}-.2673096{col 71}{space 3}  1.25497
{txt}{space 9}blocks7 {c |}{col 18}{res}{space 2}-.0667803{col 30}{space 2}  .348231{col 41}{space 1}   -0.19{col 50}{space 3}0.848{col 58}{space 4}-.7493006{col 71}{space 3}   .61574
{txt}{space 9}blocks8 {c |}{col 18}{res}{space 2} .0383105{col 30}{space 2} .4110953{col 41}{space 1}    0.09{col 50}{space 3}0.926{col 58}{space 4}-.7674215{col 71}{space 3} .8440424
{txt}{space 9}blocks9 {c |}{col 18}{res}{space 2}-.6789096{col 30}{space 2}   .74173{col 41}{space 1}   -0.92{col 50}{space 3}0.360{col 58}{space 4}-2.132674{col 71}{space 3} .7748545
{txt}{space 8}blocks10 {c |}{col 18}{res}{space 2}-.7218746{col 30}{space 2} .2860701{col 41}{space 1}   -2.52{col 50}{space 3}0.012{col 58}{space 4}-1.282562{col 71}{space 3}-.1611874
{txt}{space 8}blocks11 {c |}{col 18}{res}{space 2} .6915073{col 30}{space 2} .4770282{col 41}{space 1}    1.45{col 50}{space 3}0.147{col 58}{space 4}-.2434508{col 71}{space 3} 1.626465
{txt}{space 8}blocks12 {c |}{col 18}{res}{space 2} .4658628{col 30}{space 2} .2407149{col 41}{space 1}    1.94{col 50}{space 3}0.053{col 58}{space 4}-.0059298{col 71}{space 3} .9376553
{txt}{space 8}blocks13 {c |}{col 18}{res}{space 2} .5836511{col 30}{space 2} .2973356{col 41}{space 1}    1.96{col 50}{space 3}0.050{col 58}{space 4}  .000884{col 71}{space 3} 1.166418
{txt}{space 8}blocks14 {c |}{col 18}{res}{space 2}-.1587089{col 30}{space 2} .3506836{col 41}{space 1}   -0.45{col 50}{space 3}0.651{col 58}{space 4}-.8460362{col 71}{space 3} .5286184
{txt}{space 16} {c |}
t#c.knowledge1ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .1028402{col 30}{space 2}  .089382{col 41}{space 1}    1.15{col 50}{space 3}0.250{col 58}{space 4}-.0723453{col 71}{space 3} .2780257
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 4}t#c.femalece {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .3479948{col 30}{space 2} .2131077{col 41}{space 1}    1.63{col 50}{space 3}0.102{col 58}{space 4}-.0696886{col 71}{space 3} .7656783
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-1.035823{col 30}{space 2} .6611832{col 41}{space 1}   -1.57{col 50}{space 3}0.117{col 58}{space 4}-2.331718{col 71}{space 3} .2600723
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.2533235{col 30}{space 2} .9608724{col 41}{space 1}   -0.26{col 50}{space 3}0.792{col 58}{space 4}-2.136599{col 71}{space 3} 1.629952
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.4646564{col 30}{space 2} .5532151{col 41}{space 1}   -0.84{col 50}{space 3}0.401{col 58}{space 4}-1.548938{col 71}{space 3} .6196252
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .9471859{col 30}{space 2} .6936349{col 41}{space 1}    1.37{col 50}{space 3}0.172{col 58}{space 4}-.4123136{col 71}{space 3} 2.306685
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .3489931{col 30}{space 2}  .903477{col 41}{space 1}    0.39{col 50}{space 3}0.699{col 58}{space 4}-1.421789{col 71}{space 3} 2.119775
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .2023448{col 30}{space 2}  .589431{col 41}{space 1}    0.34{col 50}{space 3}0.731{col 58}{space 4}-.9529187{col 71}{space 3} 1.357608
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks1ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} -1.04518{col 30}{space 2} .6996199{col 41}{space 1}   -1.49{col 50}{space 3}0.135{col 58}{space 4} -2.41641{col 71}{space 3} .3260495
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .5911859{col 30}{space 2} .9219382{col 41}{space 1}    0.64{col 50}{space 3}0.521{col 58}{space 4} -1.21578{col 71}{space 3} 2.398152
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .7677406{col 30}{space 2} .4070811{col 41}{space 1}    1.89{col 50}{space 3}0.059{col 58}{space 4}-.0301237{col 71}{space 3} 1.565605
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks5ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.2098675{col 30}{space 2} .5500988{col 41}{space 1}   -0.38{col 50}{space 3}0.703{col 58}{space 4}-1.288041{col 71}{space 3} .8683063
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks6ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .0015342{col 30}{space 2} .5113235{col 41}{space 1}    0.00{col 50}{space 3}0.998{col 58}{space 4}-1.000641{col 71}{space 3}  1.00371
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks7ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.0946111{col 30}{space 2} .5734003{col 41}{space 1}   -0.17{col 50}{space 3}0.869{col 58}{space 4}-1.218455{col 71}{space 3} 1.029233
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks8ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.5060869{col 30}{space 2} .8345381{col 41}{space 1}   -0.61{col 50}{space 3}0.544{col 58}{space 4}-2.141752{col 71}{space 3} 1.129578
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks9ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .3340469{col 30}{space 2} .8454217{col 41}{space 1}    0.40{col 50}{space 3}0.693{col 58}{space 4}-1.322949{col 71}{space 3} 1.991043
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks10ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .9272307{col 30}{space 2} .4133489{col 41}{space 1}    2.24{col 50}{space 3}0.025{col 58}{space 4} .1170817{col 71}{space 3}  1.73738
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks11ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .2529259{col 30}{space 2} .5974054{col 41}{space 1}    0.42{col 50}{space 3}0.672{col 58}{space 4}-.9179673{col 71}{space 3} 1.423819
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks12ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .1887862{col 30}{space 2} .4248086{col 41}{space 1}    0.44{col 50}{space 3}0.657{col 58}{space 4}-.6438233{col 71}{space 3} 1.021396
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks13ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-1.721431{col 30}{space 2} .6461569{col 41}{space 1}   -2.66{col 50}{space 3}0.008{col 58}{space 4}-2.987875{col 71}{space 3}-.4549864
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks14ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .4709832{col 30}{space 2}  .463978{col 41}{space 1}    1.02{col 50}{space 3}0.310{col 58}{space 4} -.438397{col 71}{space 3} 1.380363
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 5}t#c.booksce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .0326164{col 30}{space 2} .1087634{col 41}{space 1}    0.30{col 50}{space 3}0.764{col 58}{space 4}-.1805559{col 71}{space 3} .2457886
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.759029{col 30}{space 2} .3128472{col 41}{space 1}    5.62{col 50}{space 3}0.000{col 58}{space 4}  1.14586{col 71}{space 3} 2.372198
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 4.28e-07{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 6.14e-09{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.24e-08{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.223166{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.00{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est3{txt} stored)
(149 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: 1.t#c.talk1ce omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks9ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  523.7795}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 526.77629}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 528.25486}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 528.31664}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 528.31665}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}     1,061

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      36{col 31}       10{col 42}     29.5{col 53}       88
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      59{col 31}        6{col 42}     18.0{col 53}       29
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}46{txt}){col 67}={col 70}{res}  1185.34
{txt}Log likelihood = {res} 528.31665{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         talk2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}t {c |}{col 16}{res}{space 2} .0027221{col 28}{space 2} .0097026{col 39}{space 1}    0.28{col 48}{space 3}0.779{col 56}{space 4}-.0162946{col 69}{space 3} .0217388
{txt}{space 9}talk1 {c |}{col 16}{res}{space 2} .6920846{col 28}{space 2} .0316493{col 39}{space 1}   21.87{col 48}{space 3}0.000{col 56}{space 4} .6300531{col 69}{space 3}  .754116
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0202007{col 28}{space 2}   .01363{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4}-.0065136{col 69}{space 3}  .046915
{txt}{space 7}father2 {c |}{col 16}{res}{space 2}-.0289462{col 28}{space 2} .0362734{col 39}{space 1}   -0.80{col 48}{space 3}0.425{col 56}{space 4}-.1000407{col 69}{space 3} .0421483
{txt}{space 7}father3 {c |}{col 16}{res}{space 2}-.0639009{col 28}{space 2} .0507306{col 39}{space 1}   -1.26{col 48}{space 3}0.208{col 56}{space 4}-.1633309{col 69}{space 3} .0355292
{txt}{space 7}father4 {c |}{col 16}{res}{space 2} -.018636{col 28}{space 2} .0296384{col 39}{space 1}   -0.63{col 48}{space 3}0.529{col 56}{space 4}-.0767261{col 69}{space 3} .0394541
{txt}{space 7}mother2 {c |}{col 16}{res}{space 2} .0263074{col 28}{space 2} .0379858{col 39}{space 1}    0.69{col 48}{space 3}0.489{col 56}{space 4}-.0481435{col 69}{space 3} .1007583
{txt}{space 7}mother3 {c |}{col 16}{res}{space 2} .0551469{col 28}{space 2} .0469087{col 39}{space 1}    1.18{col 48}{space 3}0.240{col 56}{space 4}-.0367925{col 69}{space 3} .1470862
{txt}{space 7}mother4 {c |}{col 16}{res}{space 2} .0250297{col 28}{space 2} .0301023{col 39}{space 1}    0.83{col 48}{space 3}0.406{col 56}{space 4}-.0339697{col 69}{space 3} .0840291
{txt}{space 9}books {c |}{col 16}{res}{space 2} .0058731{col 28}{space 2} .0072189{col 39}{space 1}    0.81{col 48}{space 3}0.416{col 56}{space 4}-.0082756{col 69}{space 3} .0200219
{txt}{space 7}blocks1 {c |}{col 16}{res}{space 2}-.0198193{col 28}{space 2} .0345402{col 39}{space 1}   -0.57{col 48}{space 3}0.566{col 56}{space 4}-.0875167{col 69}{space 3} .0478782
{txt}{space 7}blocks2 {c |}{col 16}{res}{space 2}-.0001405{col 28}{space 2} .0329514{col 39}{space 1}   -0.00{col 48}{space 3}0.997{col 56}{space 4} -.064724{col 69}{space 3}  .064443
{txt}{space 7}blocks3 {c |}{col 16}{res}{space 2}-.0158971{col 28}{space 2} .0289913{col 39}{space 1}   -0.55{col 48}{space 3}0.583{col 56}{space 4}-.0727191{col 69}{space 3} .0409248
{txt}{space 7}blocks4 {c |}{col 16}{res}{space 2}  .014106{col 28}{space 2} .0331576{col 39}{space 1}    0.43{col 48}{space 3}0.671{col 56}{space 4}-.0508818{col 69}{space 3} .0790938
{txt}{space 7}blocks5 {c |}{col 16}{res}{space 2} .0466405{col 28}{space 2}  .032733{col 39}{space 1}    1.42{col 48}{space 3}0.154{col 56}{space 4} -.017515{col 69}{space 3} .1107959
{txt}{space 7}blocks6 {c |}{col 16}{res}{space 2}-.0159275{col 28}{space 2} .0439565{col 39}{space 1}   -0.36{col 48}{space 3}0.717{col 56}{space 4}-.1020806{col 69}{space 3} .0702256
{txt}{space 7}blocks7 {c |}{col 16}{res}{space 2}-.0165016{col 28}{space 2} .0334778{col 39}{space 1}   -0.49{col 48}{space 3}0.622{col 56}{space 4}-.0821169{col 69}{space 3} .0491137
{txt}{space 7}blocks8 {c |}{col 16}{res}{space 2} .0775316{col 28}{space 2} .0346606{col 39}{space 1}    2.24{col 48}{space 3}0.025{col 56}{space 4} .0095981{col 69}{space 3}  .145465
{txt}{space 7}blocks9 {c |}{col 16}{res}{space 2}-.2077396{col 28}{space 2} .0613151{col 39}{space 1}   -3.39{col 48}{space 3}0.001{col 56}{space 4}-.3279151{col 69}{space 3}-.0875642
{txt}{space 6}blocks10 {c |}{col 16}{res}{space 2}-.0365217{col 28}{space 2} .0318494{col 39}{space 1}   -1.15{col 48}{space 3}0.252{col 56}{space 4}-.0989453{col 69}{space 3}  .025902
{txt}{space 6}blocks11 {c |}{col 16}{res}{space 2} .0560542{col 28}{space 2} .0425364{col 39}{space 1}    1.32{col 48}{space 3}0.188{col 56}{space 4}-.0273157{col 69}{space 3} .1394241
{txt}{space 6}blocks12 {c |}{col 16}{res}{space 2} .0076309{col 28}{space 2} .0259651{col 39}{space 1}    0.29{col 48}{space 3}0.769{col 56}{space 4}-.0432597{col 69}{space 3} .0585215
{txt}{space 6}blocks13 {c |}{col 16}{res}{space 2} -.033867{col 28}{space 2} .0293371{col 39}{space 1}   -1.15{col 48}{space 3}0.248{col 56}{space 4}-.0913666{col 69}{space 3} .0236327
{txt}{space 6}blocks14 {c |}{col 16}{res}{space 2}-.0343988{col 28}{space 2} .0305147{col 39}{space 1}   -1.13{col 48}{space 3}0.260{col 56}{space 4}-.0942065{col 69}{space 3} .0254088
{txt}{space 14} {c |}
{space 3}t#c.talk1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0383035{col 28}{space 2}  .045475{col 39}{space 1}    0.84{col 48}{space 3}0.400{col 56}{space 4}-.0508257{col 69}{space 3} .1274328
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 2}t#c.femalece {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0099169{col 28}{space 2} .0201294{col 39}{space 1}   -0.49{col 48}{space 3}0.622{col 56}{space 4}-.0493697{col 69}{space 3}  .029536
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0682723{col 28}{space 2} .0556552{col 39}{space 1}    1.23{col 48}{space 3}0.220{col 56}{space 4}-.0408098{col 69}{space 3} .1773544
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .1226507{col 28}{space 2} .0789153{col 39}{space 1}    1.55{col 48}{space 3}0.120{col 56}{space 4}-.0320206{col 69}{space 3} .2773219
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0553302{col 28}{space 2} .0453157{col 39}{space 1}    1.22{col 48}{space 3}0.222{col 56}{space 4} -.033487{col 69}{space 3} .1441473
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0584914{col 28}{space 2} .0566044{col 39}{space 1}   -1.03{col 48}{space 3}0.301{col 56}{space 4}-.1694339{col 69}{space 3} .0524511
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.1434031{col 28}{space 2} .0696705{col 39}{space 1}   -2.06{col 48}{space 3}0.040{col 56}{space 4}-.2799548{col 69}{space 3}-.0068513
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0803497{col 28}{space 2} .0461711{col 39}{space 1}   -1.74{col 48}{space 3}0.082{col 56}{space 4}-.1708434{col 69}{space 3}  .010144
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .008084{col 28}{space 2} .0505191{col 39}{space 1}    0.16{col 48}{space 3}0.873{col 56}{space 4}-.0909315{col 69}{space 3} .1070995
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .077455{col 28}{space 2} .0642827{col 39}{space 1}    1.20{col 48}{space 3}0.228{col 56}{space 4}-.0485369{col 69}{space 3} .2034468
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0242511{col 28}{space 2} .0399628{col 39}{space 1}   -0.61{col 48}{space 3}0.544{col 56}{space 4}-.1025768{col 69}{space 3} .0540745
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks5ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0878208{col 28}{space 2} .0571352{col 39}{space 1}   -1.54{col 48}{space 3}0.124{col 56}{space 4}-.1998038{col 69}{space 3} .0241622
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks6ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0043069{col 28}{space 2} .0525594{col 39}{space 1}   -0.08{col 48}{space 3}0.935{col 56}{space 4}-.1073214{col 69}{space 3} .0987075
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks7ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0024193{col 28}{space 2}  .048006{col 39}{space 1}   -0.05{col 48}{space 3}0.960{col 56}{space 4}-.0965094{col 69}{space 3} .0916708
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks8ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0230741{col 28}{space 2}  .059799{col 39}{space 1}   -0.39{col 48}{space 3}0.700{col 56}{space 4}-.1402779{col 69}{space 3} .0941297
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks9ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .171857{col 28}{space 2} .0691689{col 39}{space 1}    2.48{col 48}{space 3}0.013{col 56}{space 4} .0362884{col 69}{space 3} .3074257
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks10ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0206226{col 28}{space 2} .0442227{col 39}{space 1}    0.47{col 48}{space 3}0.641{col 56}{space 4}-.0660523{col 69}{space 3} .1072975
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks11ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0123536{col 28}{space 2} .0527324{col 39}{space 1}   -0.23{col 48}{space 3}0.815{col 56}{space 4}-.1157072{col 69}{space 3}     .091
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks12ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0620671{col 28}{space 2} .0423558{col 39}{space 1}   -1.47{col 48}{space 3}0.143{col 56}{space 4} -.145083{col 69}{space 3} .0209488
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks13ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0120571{col 28}{space 2} .0509679{col 39}{space 1}   -0.24{col 48}{space 3}0.813{col 56}{space 4}-.1119524{col 69}{space 3} .0878381
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks14ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0077325{col 28}{space 2}  .043848{col 39}{space 1}    0.18{col 48}{space 3}0.860{col 56}{space 4}-.0782079{col 69}{space 3} .0936729
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 3}t#c.booksce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0034947{col 28}{space 2}  .010476{col 39}{space 1}    0.33{col 48}{space 3}0.739{col 56}{space 4} -.017038{col 69}{space 3} .0240274
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .1206062{col 28}{space 2} .0279958{col 39}{space 1}    4.31{col 48}{space 3}0.000{col 56}{space 4} .0657354{col 69}{space 3} .1754769
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 4.76e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 9.53e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 3.65e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33}  .147065{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}4.5e-13{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est4{txt} stored)

{com}. 
. esttab using "lin1.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept level at 2)" "sd(Residuals leve at 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"lin1.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Reconstructing table A19 in appendix 11 //
. 
. // End of year, note that block 9 is removed as only one class in that block 
. // participated in the final survey. 
.         foreach k in interest values knowledge talk   {c -(}
{txt}  2{com}.         local Z  female father2-father4 mother2-mother4 books blocks1-blocks8 blocks10-blocks14
{txt}  3{com}.  quietly xtmixed `k'3 t `k'1 `Z'  ||   teacherID : t  || classID : 
{txt}  4{com}. gen sample1`k' = e(sample)
{txt}  5{com}. foreach var in `k'1  blocks1 blocks2 blocks3 blocks4 blocks5 blocks6 blocks7 blocks8 blocks9 blocks10 blocks11 blocks12 blocks13 blocks14 female father1 father2 father3 father4 mother1 mother2 mother3 mother4 books1 books2 books3 books4 books {c -(}
{txt}  6{com}. qui noisily capture drop `var'me `var'ce
{txt}  7{com}. egen `var'me = mean(cond(sample1`k'==1, `var', .))
{txt}  8{com}. gen `var'ce=`var'-`var'me
{txt}  9{com}. {c )-}
{txt} 10{com}. 
. local X  female father2-father4 mother2-mother4 books blocks1-blocks8 blocks10-blocks14
{txt} 11{com}. eststo: xtmixed `k'3 t `k'1ce `X' i.t#c.`k'1ce i.t#c.femalece i.t#c.blocks1ce   i.t#c.blocks2ce i.t#c.blocks3ce i.t#c.blocks4ce i.t#c.blocks5ce i.t#c.blocks6ce i.t#c.blocks7ce i.t#c.blocks8ce i.t#c.blocks10ce        i.t#c.blocks11ce        i.t#c.blocks12ce        i.t#c.blocks13ce        i.t#c.blocks14ce  i.t#c.father2ce i.t#c.father3ce i.t#c.father4ce  i.t#c.mother2ce i.t#c.mother3ce i.t#c.mother4ce  i.t#c.booksce ||   teacherID : t  || classID : 
{txt} 12{com}. qui noisily capture drop sample1`k'
{txt} 13{com}. {c )-}
{txt}(131 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: blocks1 omitted because of collinearity
note: blocks6 omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 0.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 180.74343}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 183.21935}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 183.34857}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 183.35351}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 183.35357}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res: 183.35357}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       867

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     26.3{col 53}       76
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     17.3{col 53}       31
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}41{txt}){col 67}={col 70}{res}   480.57
{txt}Log likelihood = {res} 183.35357{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      interest3{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}t {c |}{col 17}{res}{space 2} .0000481{col 29}{space 2} .0147362{col 40}{space 1}    0.00{col 49}{space 3}0.997{col 57}{space 4}-.0288344{col 70}{space 3} .0289306
{txt}{space 4}interest1ce {c |}{col 17}{res}{space 2} .5729426{col 29}{space 2} .0485578{col 40}{space 1}   11.80{col 49}{space 3}0.000{col 57}{space 4}  .477771{col 70}{space 3} .6681142
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0018675{col 29}{space 2} .0192081{col 40}{space 1}   -0.10{col 49}{space 3}0.923{col 57}{space 4}-.0395146{col 70}{space 3} .0357797
{txt}{space 8}father2 {c |}{col 17}{res}{space 2}-.0460824{col 29}{space 2} .0511582{col 40}{space 1}   -0.90{col 49}{space 3}0.368{col 57}{space 4}-.1463506{col 70}{space 3} .0541858
{txt}{space 8}father3 {c |}{col 17}{res}{space 2}-.1342099{col 29}{space 2} .0637053{col 40}{space 1}   -2.11{col 49}{space 3}0.035{col 57}{space 4}-.2590699{col 70}{space 3}-.0093499
{txt}{space 8}father4 {c |}{col 17}{res}{space 2}-.0126501{col 29}{space 2} .0419168{col 40}{space 1}   -0.30{col 49}{space 3}0.763{col 57}{space 4}-.0948056{col 70}{space 3} .0695054
{txt}{space 8}mother2 {c |}{col 17}{res}{space 2} .0476359{col 29}{space 2} .0532168{col 40}{space 1}    0.90{col 49}{space 3}0.371{col 57}{space 4} -.056667{col 70}{space 3} .1519389
{txt}{space 8}mother3 {c |}{col 17}{res}{space 2} .0773462{col 29}{space 2} .0664322{col 40}{space 1}    1.16{col 49}{space 3}0.244{col 57}{space 4}-.0528586{col 70}{space 3}  .207551
{txt}{space 8}mother4 {c |}{col 17}{res}{space 2} .0111055{col 29}{space 2} .0427698{col 40}{space 1}    0.26{col 49}{space 3}0.795{col 57}{space 4}-.0727218{col 70}{space 3} .0949327
{txt}{space 10}books {c |}{col 17}{res}{space 2} .0322573{col 29}{space 2} .0097835{col 40}{space 1}    3.30{col 49}{space 3}0.001{col 57}{space 4}  .013082{col 70}{space 3} .0514325
{txt}{space 8}blocks1 {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 8}blocks2 {c |}{col 17}{res}{space 2}-.0972178{col 29}{space 2} .0561567{col 40}{space 1}   -1.73{col 49}{space 3}0.083{col 57}{space 4}-.2072828{col 70}{space 3} .0128473
{txt}{space 8}blocks3 {c |}{col 17}{res}{space 2}-.0153557{col 29}{space 2} .0418794{col 40}{space 1}   -0.37{col 49}{space 3}0.714{col 57}{space 4}-.0974378{col 70}{space 3} .0667265
{txt}{space 8}blocks4 {c |}{col 17}{res}{space 2} .0253879{col 29}{space 2} .0617125{col 40}{space 1}    0.41{col 49}{space 3}0.681{col 57}{space 4}-.0955663{col 70}{space 3} .1463421
{txt}{space 8}blocks5 {c |}{col 17}{res}{space 2}-.0431243{col 29}{space 2} .0424295{col 40}{space 1}   -1.02{col 49}{space 3}0.309{col 57}{space 4}-.1262846{col 70}{space 3} .0400361
{txt}{space 8}blocks6 {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 8}blocks7 {c |}{col 17}{res}{space 2}-.0416219{col 29}{space 2} .0446574{col 40}{space 1}   -0.93{col 49}{space 3}0.351{col 57}{space 4}-.1291488{col 70}{space 3}  .045905
{txt}{space 8}blocks8 {c |}{col 17}{res}{space 2}-.0376028{col 29}{space 2} .0464237{col 40}{space 1}   -0.81{col 49}{space 3}0.418{col 57}{space 4}-.1285917{col 70}{space 3} .0533861
{txt}{space 7}blocks10 {c |}{col 17}{res}{space 2}-.0192338{col 29}{space 2} .0483069{col 40}{space 1}   -0.40{col 49}{space 3}0.691{col 57}{space 4}-.1139136{col 70}{space 3}  .075446
{txt}{space 7}blocks11 {c |}{col 17}{res}{space 2} .0059432{col 29}{space 2} .0577627{col 40}{space 1}    0.10{col 49}{space 3}0.918{col 57}{space 4}-.1072696{col 70}{space 3} .1191559
{txt}{space 7}blocks12 {c |}{col 17}{res}{space 2} .0075597{col 29}{space 2} .0341448{col 40}{space 1}    0.22{col 49}{space 3}0.825{col 57}{space 4}-.0593629{col 70}{space 3} .0744823
{txt}{space 7}blocks13 {c |}{col 17}{res}{space 2}-.0142364{col 29}{space 2} .0404202{col 40}{space 1}   -0.35{col 49}{space 3}0.725{col 57}{space 4}-.0934585{col 70}{space 3} .0649857
{txt}{space 7}blocks14 {c |}{col 17}{res}{space 2}-.0283074{col 29}{space 2} .0440149{col 40}{space 1}   -0.64{col 49}{space 3}0.520{col 57}{space 4} -.114575{col 70}{space 3} .0579601
{txt}{space 15} {c |}
t#c.interest1ce {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .0288266{col 29}{space 2} .0623461{col 40}{space 1}    0.46{col 49}{space 3}0.644{col 57}{space 4}-.0933696{col 70}{space 3} .1510228
{txt}{space 15} {c |}
{space 3}t#c.femalece {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0000801{col 29}{space 2} .0304994{col 40}{space 1}   -0.00{col 49}{space 3}0.998{col 57}{space 4}-.0598579{col 70}{space 3} .0596976
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks1ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0518076{col 29}{space 2} .0795509{col 40}{space 1}    0.65{col 49}{space 3}0.515{col 57}{space 4}-.1041093{col 70}{space 3} .2077246
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0406141{col 29}{space 2} .0809936{col 40}{space 1}    0.50{col 49}{space 3}0.616{col 57}{space 4}-.1181305{col 70}{space 3} .1993587
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0033788{col 29}{space 2} .0694084{col 40}{space 1}    0.05{col 49}{space 3}0.961{col 57}{space 4}-.1326592{col 70}{space 3} .1394167
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks5ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0270326{col 29}{space 2}  .078979{col 40}{space 1}   -0.34{col 49}{space 3}0.732{col 57}{space 4}-.1818286{col 70}{space 3} .1277635
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks6ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks7ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0295205{col 29}{space 2} .0814112{col 40}{space 1}   -0.36{col 49}{space 3}0.717{col 57}{space 4}-.1890836{col 70}{space 3} .1300426
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.blocks8ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}  .022044{col 29}{space 2} .0800235{col 40}{space 1}    0.28{col 49}{space 3}0.783{col 57}{space 4}-.1347992{col 70}{space 3} .1788873
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks10ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .1211855{col 29}{space 2} .0663229{col 40}{space 1}    1.83{col 49}{space 3}0.068{col 57}{space 4}-.0088051{col 70}{space 3}  .251176
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks11ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0167643{col 29}{space 2} .0732748{col 40}{space 1}   -0.23{col 49}{space 3}0.819{col 57}{space 4}-.1603803{col 70}{space 3} .1268518
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks12ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0765018{col 29}{space 2} .0574603{col 40}{space 1}    1.33{col 49}{space 3}0.183{col 57}{space 4}-.0361183{col 70}{space 3} .1891219
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks13ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0089269{col 29}{space 2} .0682288{col 40}{space 1}   -0.13{col 49}{space 3}0.896{col 57}{space 4}-.1426529{col 70}{space 3}  .124799
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 1}t#c.blocks14ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0188409{col 29}{space 2} .0630615{col 40}{space 1}   -0.30{col 49}{space 3}0.765{col 57}{space 4}-.1424391{col 70}{space 3} .1047573
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0751596{col 29}{space 2}  .092115{col 40}{space 1}    0.82{col 49}{space 3}0.415{col 57}{space 4}-.1053825{col 70}{space 3} .2557018
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}  .395103{col 29}{space 2} .1423641{col 40}{space 1}    2.78{col 49}{space 3}0.006{col 57}{space 4} .1160746{col 70}{space 3} .6741315
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.father4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0288669{col 29}{space 2} .0715393{col 40}{space 1}   -0.40{col 49}{space 3}0.687{col 57}{space 4}-.1690812{col 70}{space 3} .1113475
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother2ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0582451{col 29}{space 2} .0921566{col 40}{space 1}   -0.63{col 49}{space 3}0.527{col 57}{space 4}-.2388687{col 70}{space 3} .1223786
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother3ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}  .060149{col 29}{space 2} .1032812{col 40}{space 1}    0.58{col 49}{space 3}0.560{col 57}{space 4}-.1422785{col 70}{space 3} .2625764
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 2}t#c.mother4ce {c |}
{space 13}0  {c |}{col 17}{res}{space 2} .0492078{col 29}{space 2} .0713064{col 40}{space 1}    0.69{col 49}{space 3}0.490{col 57}{space 4}-.0905501{col 70}{space 3} .1889657
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 4}t#c.booksce {c |}
{space 13}0  {c |}{col 17}{res}{space 2}-.0360617{col 29}{space 2} .0157265{col 40}{space 1}   -2.29{col 49}{space 3}0.022{col 57}{space 4}-.0668851{col 70}{space 3}-.0052384
{txt}{space 13}1  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (omitted)
{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .5832103{col 29}{space 2} .0368505{col 40}{space 1}   15.83{col 49}{space 3}0.000{col 57}{space 4} .5109846{col 70}{space 3} .6554359
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 1.43e-11{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.26e-12{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.22e-10{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1958475{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}1.2e-12{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est1{txt} stored)
(204 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: blocks1 omitted because of collinearity
note: blocks6 omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 0.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 766.77612}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 769.05095}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:  770.6613}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 770.82949}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 770.82962}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res: 770.82962}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       783

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     23.7{col 53}       71
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     15.7{col 53}       30
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}41{txt}){col 67}={col 70}{res}   416.82
{txt}Log likelihood = {res} 770.82962{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       values3{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}t {c |}{col 16}{res}{space 2} .0004759{col 28}{space 2} .0072201{col 39}{space 1}    0.07{col 48}{space 3}0.947{col 56}{space 4}-.0136752{col 69}{space 3} .0146271
{txt}{space 5}values1ce {c |}{col 16}{res}{space 2}   .54527{col 28}{space 2} .0530583{col 39}{space 1}   10.28{col 48}{space 3}0.000{col 56}{space 4} .4412777{col 69}{space 3} .6492622
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0146423{col 28}{space 2} .0093084{col 39}{space 1}    1.57{col 48}{space 3}0.116{col 56}{space 4}-.0036018{col 69}{space 3} .0328864
{txt}{space 7}father2 {c |}{col 16}{res}{space 2}-.0058078{col 28}{space 2} .0245972{col 39}{space 1}   -0.24{col 48}{space 3}0.813{col 56}{space 4}-.0540174{col 69}{space 3} .0424018
{txt}{space 7}father3 {c |}{col 16}{res}{space 2} .0102864{col 28}{space 2} .0314822{col 39}{space 1}    0.33{col 48}{space 3}0.744{col 56}{space 4}-.0514176{col 69}{space 3} .0719903
{txt}{space 7}father4 {c |}{col 16}{res}{space 2} .0158116{col 28}{space 2} .0200232{col 39}{space 1}    0.79{col 48}{space 3}0.430{col 56}{space 4}-.0234332{col 69}{space 3} .0550563
{txt}{space 7}mother2 {c |}{col 16}{res}{space 2} .0308243{col 28}{space 2} .0255889{col 39}{space 1}    1.20{col 48}{space 3}0.228{col 56}{space 4}-.0193291{col 69}{space 3} .0809776
{txt}{space 7}mother3 {c |}{col 16}{res}{space 2} .0182306{col 28}{space 2} .0323774{col 39}{space 1}    0.56{col 48}{space 3}0.573{col 56}{space 4} -.045228{col 69}{space 3} .0816891
{txt}{space 7}mother4 {c |}{col 16}{res}{space 2} .0014344{col 28}{space 2} .0202522{col 39}{space 1}    0.07{col 48}{space 3}0.944{col 56}{space 4}-.0382592{col 69}{space 3} .0411279
{txt}{space 9}books {c |}{col 16}{res}{space 2} .0104435{col 28}{space 2} .0047299{col 39}{space 1}    2.21{col 48}{space 3}0.027{col 56}{space 4}  .001173{col 69}{space 3}  .019714
{txt}{space 7}blocks1 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 7}blocks2 {c |}{col 16}{res}{space 2} .0216056{col 28}{space 2} .0270154{col 39}{space 1}    0.80{col 48}{space 3}0.424{col 56}{space 4}-.0313436{col 69}{space 3} .0745549
{txt}{space 7}blocks3 {c |}{col 16}{res}{space 2}-.0289287{col 28}{space 2} .0202706{col 39}{space 1}   -1.43{col 48}{space 3}0.154{col 56}{space 4}-.0686584{col 69}{space 3}  .010801
{txt}{space 7}blocks4 {c |}{col 16}{res}{space 2} .0094412{col 28}{space 2} .0298522{col 39}{space 1}    0.32{col 48}{space 3}0.752{col 56}{space 4} -.049068{col 69}{space 3} .0679503
{txt}{space 7}blocks5 {c |}{col 16}{res}{space 2}  .024006{col 28}{space 2} .0201298{col 39}{space 1}    1.19{col 48}{space 3}0.233{col 56}{space 4}-.0154477{col 69}{space 3} .0634597
{txt}{space 7}blocks6 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 7}blocks7 {c |}{col 16}{res}{space 2}-.0349734{col 28}{space 2} .0226478{col 39}{space 1}   -1.54{col 48}{space 3}0.123{col 56}{space 4}-.0793623{col 69}{space 3} .0094154
{txt}{space 7}blocks8 {c |}{col 16}{res}{space 2}-.0238442{col 28}{space 2} .0221025{col 39}{space 1}   -1.08{col 48}{space 3}0.281{col 56}{space 4}-.0671644{col 69}{space 3}  .019476
{txt}{space 6}blocks10 {c |}{col 16}{res}{space 2} .0105271{col 28}{space 2} .0244088{col 39}{space 1}    0.43{col 48}{space 3}0.666{col 56}{space 4}-.0373133{col 69}{space 3} .0583674
{txt}{space 6}blocks11 {c |}{col 16}{res}{space 2} .0144538{col 28}{space 2} .0278082{col 39}{space 1}    0.52{col 48}{space 3}0.603{col 56}{space 4}-.0400493{col 69}{space 3} .0689569
{txt}{space 6}blocks12 {c |}{col 16}{res}{space 2} .0186094{col 28}{space 2} .0168689{col 39}{space 1}    1.10{col 48}{space 3}0.270{col 56}{space 4}-.0144529{col 69}{space 3} .0516718
{txt}{space 6}blocks13 {c |}{col 16}{res}{space 2}-.0291556{col 28}{space 2} .0193312{col 39}{space 1}   -1.51{col 48}{space 3}0.131{col 56}{space 4} -.067044{col 69}{space 3} .0087329
{txt}{space 6}blocks14 {c |}{col 16}{res}{space 2}-.0123158{col 28}{space 2} .0208125{col 39}{space 1}   -0.59{col 48}{space 3}0.554{col 56}{space 4}-.0531076{col 69}{space 3} .0284759
{txt}{space 14} {c |}
{space 1}t#c.values1ce {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0375574{col 28}{space 2}  .068121{col 39}{space 1}   -0.55{col 48}{space 3}0.581{col 56}{space 4}-.1710721{col 69}{space 3} .0959572
{txt}{space 14} {c |}
{space 2}t#c.femalece {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0152067{col 28}{space 2} .0147414{col 39}{space 1}    1.03{col 48}{space 3}0.302{col 56}{space 4}-.0136859{col 69}{space 3} .0440994
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0330372{col 28}{space 2} .0419741{col 39}{space 1}   -0.79{col 48}{space 3}0.431{col 56}{space 4}-.1153049{col 69}{space 3} .0492305
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0691471{col 28}{space 2} .0378883{col 39}{space 1}    1.83{col 48}{space 3}0.068{col 56}{space 4}-.0051127{col 69}{space 3} .1434069
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0029794{col 28}{space 2} .0335751{col 39}{space 1}   -0.09{col 48}{space 3}0.929{col 56}{space 4}-.0687853{col 69}{space 3} .0628265
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks5ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0249023{col 28}{space 2} .0380579{col 39}{space 1}    0.65{col 48}{space 3}0.513{col 56}{space 4}-.0496899{col 69}{space 3} .0994945
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks6ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks7ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0789373{col 28}{space 2} .0403944{col 39}{space 1}    1.95{col 48}{space 3}0.051{col 56}{space 4}-.0002342{col 69}{space 3} .1581089
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks8ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0403214{col 28}{space 2} .0388885{col 39}{space 1}   -1.04{col 48}{space 3}0.300{col 56}{space 4}-.1165415{col 69}{space 3} .0358987
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks10ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .058296{col 28}{space 2} .0323994{col 39}{space 1}    1.80{col 48}{space 3}0.072{col 56}{space 4}-.0052057{col 69}{space 3} .1217977
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks11ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0673221{col 28}{space 2} .0356928{col 39}{space 1}   -1.89{col 48}{space 3}0.059{col 56}{space 4}-.1372787{col 69}{space 3} .0026345
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks12ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0112857{col 28}{space 2}  .029022{col 39}{space 1}    0.39{col 48}{space 3}0.697{col 56}{space 4}-.0455962{col 69}{space 3} .0681677
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks13ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0151205{col 28}{space 2} .0363808{col 39}{space 1}    0.42{col 48}{space 3}0.678{col 56}{space 4}-.0561845{col 69}{space 3} .0864255
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks14ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0408838{col 28}{space 2}      .03{col 39}{space 1}    1.36{col 48}{space 3}0.173{col 56}{space 4}-.0179151{col 69}{space 3} .0996827
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0061634{col 28}{space 2} .0443105{col 39}{space 1}    0.14{col 48}{space 3}0.889{col 56}{space 4}-.0806836{col 69}{space 3} .0930103
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0106849{col 28}{space 2} .0671798{col 39}{space 1}    0.16{col 48}{space 3}0.874{col 56}{space 4} -.120985{col 69}{space 3} .1423549
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0337258{col 28}{space 2} .0347899{col 39}{space 1}   -0.97{col 48}{space 3}0.332{col 56}{space 4}-.1019127{col 69}{space 3}  .034461
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0367029{col 28}{space 2}  .043866{col 39}{space 1}   -0.84{col 48}{space 3}0.403{col 56}{space 4}-.1226787{col 69}{space 3} .0492729
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0505095{col 28}{space 2} .0492961{col 39}{space 1}   -1.02{col 48}{space 3}0.306{col 56}{space 4} -.147128{col 69}{space 3}  .046109
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0211066{col 28}{space 2} .0339994{col 39}{space 1}    0.62{col 48}{space 3}0.535{col 56}{space 4}-.0455309{col 69}{space 3} .0877442
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 3}t#c.booksce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0052607{col 28}{space 2} .0076753{col 39}{space 1}    0.69{col 48}{space 3}0.493{col 56}{space 4}-.0097827{col 69}{space 3} .0203041
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}  .771539{col 28}{space 2} .0182937{col 39}{space 1}   42.18{col 48}{space 3}0.000{col 56}{space 4}  .735684{col 69}{space 3} .8073939
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 8.22e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 4.04e-14{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.95e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .0904105{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}1.8e-12{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est2{txt} stored)
(516 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: blocks1 omitted because of collinearity
note: blocks6 omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 0.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -952.5586}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-951.22801}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: -951.0502}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-951.03157}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-951.03142}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res:-951.03142}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       553

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        1{col 42}     16.8{col 53}       52
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        1{col 42}     11.1{col 53}       25
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}41{txt}){col 67}={col 70}{res}   149.34
{txt}Log likelihood = {res}-951.03142{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      knowledge3{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}t {c |}{col 18}{res}{space 2} .3470668{col 30}{space 2} .1577784{col 41}{space 1}    2.20{col 50}{space 3}0.028{col 58}{space 4} .0378268{col 71}{space 3} .6563067
{txt}{space 4}knowledge1ce {c |}{col 18}{res}{space 2} .3694369{col 30}{space 2} .0845364{col 41}{space 1}    4.37{col 50}{space 3}0.000{col 58}{space 4} .2037485{col 71}{space 3} .5351253
{txt}{space 10}female {c |}{col 18}{res}{space 2}-.5077013{col 30}{space 2} .1616235{col 41}{space 1}   -3.14{col 50}{space 3}0.002{col 58}{space 4}-.8244775{col 71}{space 3} -.190925
{txt}{space 9}father2 {c |}{col 18}{res}{space 2}-.2314663{col 30}{space 2}  .417495{col 41}{space 1}   -0.55{col 50}{space 3}0.579{col 58}{space 4}-1.049741{col 71}{space 3} .5868088
{txt}{space 9}father3 {c |}{col 18}{res}{space 2} .0995209{col 30}{space 2} .5403962{col 41}{space 1}    0.18{col 50}{space 3}0.854{col 58}{space 4}-.9596362{col 71}{space 3} 1.158678
{txt}{space 9}father4 {c |}{col 18}{res}{space 2}-.0589735{col 30}{space 2} .3580837{col 41}{space 1}   -0.16{col 50}{space 3}0.869{col 58}{space 4}-.7608046{col 71}{space 3} .6428576
{txt}{space 9}mother2 {c |}{col 18}{res}{space 2} .7657946{col 30}{space 2} .4506385{col 41}{space 1}    1.70{col 50}{space 3}0.089{col 58}{space 4}-.1174406{col 71}{space 3}  1.64903
{txt}{space 9}mother3 {c |}{col 18}{res}{space 2} 1.575178{col 30}{space 2} .6712876{col 41}{space 1}    2.35{col 50}{space 3}0.019{col 58}{space 4} .2594782{col 71}{space 3} 2.890877
{txt}{space 9}mother4 {c |}{col 18}{res}{space 2} .3815905{col 30}{space 2} .3679553{col 41}{space 1}    1.04{col 50}{space 3}0.300{col 58}{space 4}-.3395886{col 71}{space 3}  1.10277
{txt}{space 11}books {c |}{col 18}{res}{space 2} .1123225{col 30}{space 2}  .083047{col 41}{space 1}    1.35{col 50}{space 3}0.176{col 58}{space 4}-.0504466{col 71}{space 3} .2750916
{txt}{space 9}blocks1 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 9}blocks2 {c |}{col 18}{res}{space 2}-.3830669{col 30}{space 2} .6804184{col 41}{space 1}   -0.56{col 50}{space 3}0.573{col 58}{space 4}-1.716662{col 71}{space 3} .9505286
{txt}{space 9}blocks3 {c |}{col 18}{res}{space 2} 1.415555{col 30}{space 2} .4194831{col 41}{space 1}    3.37{col 50}{space 3}0.001{col 58}{space 4}  .593383{col 71}{space 3} 2.237727
{txt}{space 9}blocks4 {c |}{col 18}{res}{space 2} .0213759{col 30}{space 2} .6409774{col 41}{space 1}    0.03{col 50}{space 3}0.973{col 58}{space 4}-1.234917{col 71}{space 3} 1.277668
{txt}{space 9}blocks5 {c |}{col 18}{res}{space 2} .1551065{col 30}{space 2} .4710083{col 41}{space 1}    0.33{col 50}{space 3}0.742{col 58}{space 4}-.7680528{col 71}{space 3} 1.078266
{txt}{space 9}blocks6 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 9}blocks7 {c |}{col 18}{res}{space 2}  .147558{col 30}{space 2} .4653771{col 41}{space 1}    0.32{col 50}{space 3}0.751{col 58}{space 4}-.7645643{col 71}{space 3}  1.05968
{txt}{space 9}blocks8 {c |}{col 18}{res}{space 2} -.717629{col 30}{space 2} .5070035{col 41}{space 1}   -1.42{col 50}{space 3}0.157{col 58}{space 4}-1.711338{col 71}{space 3} .2760796
{txt}{space 8}blocks10 {c |}{col 18}{res}{space 2} .6554685{col 30}{space 2} .4521068{col 41}{space 1}    1.45{col 50}{space 3}0.147{col 58}{space 4}-.2306446{col 71}{space 3} 1.541582
{txt}{space 8}blocks11 {c |}{col 18}{res}{space 2} .3747459{col 30}{space 2} .6017728{col 41}{space 1}    0.62{col 50}{space 3}0.533{col 58}{space 4}-.8047071{col 71}{space 3} 1.554199
{txt}{space 8}blocks12 {c |}{col 18}{res}{space 2} .1768457{col 30}{space 2} .3897619{col 41}{space 1}    0.45{col 50}{space 3}0.650{col 58}{space 4}-.5870736{col 71}{space 3}  .940765
{txt}{space 8}blocks13 {c |}{col 18}{res}{space 2}-.6557433{col 30}{space 2} .4653282{col 41}{space 1}   -1.41{col 50}{space 3}0.159{col 58}{space 4} -1.56777{col 71}{space 3} .2562831
{txt}{space 8}blocks14 {c |}{col 18}{res}{space 2}-1.026596{col 30}{space 2}  .507324{col 41}{space 1}   -2.02{col 50}{space 3}0.043{col 58}{space 4}-2.020933{col 71}{space 3}-.0322594
{txt}{space 16} {c |}
t#c.knowledge1ce {c |}
{space 14}1  {c |}{col 18}{res}{space 2}  .030919{col 30}{space 2} .1058685{col 41}{space 1}    0.29{col 50}{space 3}0.770{col 58}{space 4}-.1765794{col 71}{space 3} .2384173
{txt}{space 16} {c |}
{space 4}t#c.femalece {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .5258948{col 30}{space 2} .2633581{col 41}{space 1}    2.00{col 50}{space 3}0.046{col 58}{space 4} .0097223{col 71}{space 3} 1.042067
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks1ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .6908452{col 30}{space 2} .8373639{col 41}{space 1}    0.83{col 50}{space 3}0.409{col 58}{space 4}-.9503578{col 71}{space 3} 2.332048
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-2.358942{col 30}{space 2} 1.417268{col 41}{space 1}   -1.66{col 50}{space 3}0.096{col 58}{space 4}-5.136736{col 71}{space 3} .4188527
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .3896059{col 30}{space 2} .6998118{col 41}{space 1}    0.56{col 50}{space 3}0.578{col 58}{space 4}-.9820001{col 71}{space 3} 1.761212
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks5ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .3741276{col 30}{space 2} .7546302{col 41}{space 1}    0.50{col 50}{space 3}0.620{col 58}{space 4} -1.10492{col 71}{space 3} 1.853176
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks6ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks7ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.7284638{col 30}{space 2} .8545063{col 41}{space 1}   -0.85{col 50}{space 3}0.394{col 58}{space 4}-2.403265{col 71}{space 3} .9463377
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.blocks8ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.9083994{col 30}{space 2} .8630786{col 41}{space 1}   -1.05{col 50}{space 3}0.293{col 58}{space 4}-2.600002{col 71}{space 3} .7832036
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks10ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.2153584{col 30}{space 2} .5803461{col 41}{space 1}   -0.37{col 50}{space 3}0.711{col 58}{space 4}-1.352816{col 71}{space 3} .9220991
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks11ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} -.498259{col 30}{space 2} .7459041{col 41}{space 1}   -0.67{col 50}{space 3}0.504{col 58}{space 4}-1.960204{col 71}{space 3}  .963686
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks12ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .1232129{col 30}{space 2} .5443032{col 41}{space 1}    0.23{col 50}{space 3}0.821{col 58}{space 4}-.9436017{col 71}{space 3} 1.190027
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks13ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .6193497{col 30}{space 2}  .939853{col 41}{space 1}    0.66{col 50}{space 3}0.510{col 58}{space 4}-1.222728{col 71}{space 3} 2.461428
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 2}t#c.blocks14ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .8457889{col 30}{space 2} .6142853{col 41}{space 1}    1.38{col 50}{space 3}0.169{col 58}{space 4}-.3581882{col 71}{space 3} 2.049766
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .9981203{col 30}{space 2} .9141136{col 41}{space 1}    1.09{col 50}{space 3}0.275{col 58}{space 4}-.7935095{col 71}{space 3}  2.78975
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} 2.006017{col 30}{space 2} 1.566651{col 41}{space 1}    1.28{col 50}{space 3}0.200{col 58}{space 4}-1.064562{col 71}{space 3} 5.076596
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.father4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .6954632{col 30}{space 2} .6665557{col 41}{space 1}    1.04{col 50}{space 3}0.297{col 58}{space 4} -.610962{col 71}{space 3} 2.001888
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother2ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-1.058829{col 30}{space 2} .9117891{col 41}{space 1}   -1.16{col 50}{space 3}0.246{col 58}{space 4}-2.845902{col 71}{space 3} .7282454
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother3ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-1.568906{col 30}{space 2} 1.013126{col 41}{space 1}   -1.55{col 50}{space 3}0.121{col 58}{space 4}-3.554597{col 71}{space 3} .4167843
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 3}t#c.mother4ce {c |}
{space 14}0  {c |}{col 18}{res}{space 2}-.6280967{col 30}{space 2}  .683983{col 41}{space 1}   -0.92{col 50}{space 3}0.358{col 58}{space 4}-1.968679{col 71}{space 3} .7124854
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 5}t#c.booksce {c |}
{space 14}0  {c |}{col 18}{res}{space 2} .0389867{col 30}{space 2} .1360738{col 41}{space 1}    0.29{col 50}{space 3}0.774{col 58}{space 4}-.2277131{col 71}{space 3} .3056864
{txt}{space 14}1  {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 16} {c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.781001{col 30}{space 2} .3382152{col 41}{space 1}   11.18{col 50}{space 3}0.000{col 58}{space 4} 3.118112{col 71}{space 3} 4.443891
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} .2830801{col 44} .1061797{col 58}  .135717{col 70} .5904514
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 3.48e-11{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.39e-08{col 44} 3.82e-08{col 58} 6.34e-11{col 70} 3.05e-06
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.336963{col 44} .0410513{col 58} 1.258877{col 70} 1.419892
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}3.48{col 59}{txt}Prob > chi2 ={col 73}{res}0.3234

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est3{txt} stored)
(149 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(160 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(153 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
(165 missing values generated)
note: blocks1 omitted because of collinearity
note: blocks6 omitted because of collinearity
note: 1.t#c.femalece omitted because of collinearity
note: 0.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks1ce omitted because of collinearity
note: 1.t#c.blocks2ce omitted because of collinearity
note: 1.t#c.blocks3ce omitted because of collinearity
note: 1.t#c.blocks4ce omitted because of collinearity
note: 1.t#c.blocks5ce omitted because of collinearity
note: 0.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks6ce omitted because of collinearity
note: 1.t#c.blocks7ce omitted because of collinearity
note: 1.t#c.blocks8ce omitted because of collinearity
note: 1.t#c.blocks10ce omitted because of collinearity
note: 1.t#c.blocks11ce omitted because of collinearity
note: 1.t#c.blocks12ce omitted because of collinearity
note: 1.t#c.blocks13ce omitted because of collinearity
note: 1.t#c.blocks14ce omitted because of collinearity
note: 1.t#c.father2ce omitted because of collinearity
note: 1.t#c.father3ce omitted because of collinearity
note: 1.t#c.father4ce omitted because of collinearity
note: 1.t#c.mother2ce omitted because of collinearity
note: 1.t#c.mother3ce omitted because of collinearity
note: 1.t#c.mother4ce omitted because of collinearity
note: 1.t#c.booksce omitted because of collinearity
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: 322.29865}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: 325.31026}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: 325.81821}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: 325.82224}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: 325.82224}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       839

{txt}{hline 16}{c TT}{hline 44}
{col 17}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{hline 16}{c +}{hline 44}
{res}{col 7}teacherID{txt}{col 17}{c |}{res}{col 21}      33{col 31}        2{col 42}     25.4{col 53}       75
{col 9}classID{txt}{col 17}{c |}{res}{col 21}      50{col 31}        2{col 42}     16.8{col 53}       27
{txt}{hline 16}{c BT}{hline 44}

{col 49}Wald chi2({res}41{txt}){col 67}={col 70}{res}   606.29
{txt}Log likelihood = {res} 325.82224{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         talk3{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}t {c |}{col 16}{res}{space 2} .0029492{col 28}{space 2} .0125538{col 39}{space 1}    0.23{col 48}{space 3}0.814{col 56}{space 4}-.0216558{col 69}{space 3} .0275542
{txt}{space 7}talk1ce {c |}{col 16}{res}{space 2} .5734275{col 28}{space 2} .0486522{col 39}{space 1}   11.79{col 48}{space 3}0.000{col 56}{space 4} .4780709{col 69}{space 3}  .668784
{txt}{space 8}female {c |}{col 16}{res}{space 2}-.0064899{col 28}{space 2} .0164561{col 39}{space 1}   -0.39{col 48}{space 3}0.693{col 56}{space 4}-.0387434{col 69}{space 3} .0257635
{txt}{space 7}father2 {c |}{col 16}{res}{space 2}-.0292976{col 28}{space 2} .0434851{col 39}{space 1}   -0.67{col 48}{space 3}0.500{col 56}{space 4}-.1145268{col 69}{space 3} .0559315
{txt}{space 7}father3 {c |}{col 16}{res}{space 2}-.0620442{col 28}{space 2} .0549802{col 39}{space 1}   -1.13{col 48}{space 3}0.259{col 56}{space 4}-.1698034{col 69}{space 3}  .045715
{txt}{space 7}father4 {c |}{col 16}{res}{space 2} .0113541{col 28}{space 2} .0358443{col 39}{space 1}    0.32{col 48}{space 3}0.751{col 56}{space 4}-.0588994{col 69}{space 3} .0816075
{txt}{space 7}mother2 {c |}{col 16}{res}{space 2} .0622437{col 28}{space 2} .0454996{col 39}{space 1}    1.37{col 48}{space 3}0.171{col 56}{space 4}-.0269338{col 69}{space 3} .1514213
{txt}{space 7}mother3 {c |}{col 16}{res}{space 2} .0289255{col 28}{space 2} .0563243{col 39}{space 1}    0.51{col 48}{space 3}0.608{col 56}{space 4}-.0814681{col 69}{space 3} .1393191
{txt}{space 7}mother4 {c |}{col 16}{res}{space 2}  .008937{col 28}{space 2} .0368588{col 39}{space 1}    0.24{col 48}{space 3}0.808{col 56}{space 4}-.0633049{col 69}{space 3} .0811789
{txt}{space 9}books {c |}{col 16}{res}{space 2} .0085514{col 28}{space 2} .0084721{col 39}{space 1}    1.01{col 48}{space 3}0.313{col 56}{space 4}-.0080536{col 69}{space 3} .0251563
{txt}{space 7}blocks1 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 7}blocks2 {c |}{col 16}{res}{space 2}-.0530477{col 28}{space 2}   .04714{col 39}{space 1}   -1.13{col 48}{space 3}0.260{col 56}{space 4}-.1454404{col 69}{space 3}  .039345
{txt}{space 7}blocks3 {c |}{col 16}{res}{space 2}-.0028722{col 28}{space 2} .0359684{col 39}{space 1}   -0.08{col 48}{space 3}0.936{col 56}{space 4} -.073369{col 69}{space 3} .0676246
{txt}{space 7}blocks4 {c |}{col 16}{res}{space 2}-.0215391{col 28}{space 2} .0517853{col 39}{space 1}   -0.42{col 48}{space 3}0.677{col 56}{space 4}-.1230364{col 69}{space 3} .0799582
{txt}{space 7}blocks5 {c |}{col 16}{res}{space 2} -.043199{col 28}{space 2}  .037512{col 39}{space 1}   -1.15{col 48}{space 3}0.249{col 56}{space 4}-.1167211{col 69}{space 3} .0303231
{txt}{space 7}blocks6 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 7}blocks7 {c |}{col 16}{res}{space 2} .0000954{col 28}{space 2} .0382435{col 39}{space 1}    0.00{col 48}{space 3}0.998{col 56}{space 4}-.0748604{col 69}{space 3} .0750512
{txt}{space 7}blocks8 {c |}{col 16}{res}{space 2}-.0146332{col 28}{space 2}  .039922{col 39}{space 1}   -0.37{col 48}{space 3}0.714{col 56}{space 4} -.092879{col 69}{space 3} .0636126
{txt}{space 6}blocks10 {c |}{col 16}{res}{space 2}-.0107547{col 28}{space 2} .0418638{col 39}{space 1}   -0.26{col 48}{space 3}0.797{col 56}{space 4}-.0928063{col 69}{space 3}  .071297
{txt}{space 6}blocks11 {c |}{col 16}{res}{space 2} .0469169{col 28}{space 2} .0487212{col 39}{space 1}    0.96{col 48}{space 3}0.336{col 56}{space 4}-.0485748{col 69}{space 3} .1424087
{txt}{space 6}blocks12 {c |}{col 16}{res}{space 2} .0132723{col 28}{space 2} .0294021{col 39}{space 1}    0.45{col 48}{space 3}0.652{col 56}{space 4}-.0443547{col 69}{space 3} .0708994
{txt}{space 6}blocks13 {c |}{col 16}{res}{space 2} -.003644{col 28}{space 2} .0341052{col 39}{space 1}   -0.11{col 48}{space 3}0.915{col 56}{space 4}-.0704888{col 69}{space 3} .0632009
{txt}{space 6}blocks14 {c |}{col 16}{res}{space 2} .0397161{col 28}{space 2} .0378028{col 39}{space 1}    1.05{col 48}{space 3}0.293{col 56}{space 4} -.034376{col 69}{space 3} .1138082
{txt}{space 14} {c |}
{space 3}t#c.talk1ce {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .1049266{col 28}{space 2} .0613304{col 39}{space 1}    1.71{col 48}{space 3}0.087{col 56}{space 4}-.0152789{col 69}{space 3}  .225132
{txt}{space 14} {c |}
{space 2}t#c.femalece {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0078004{col 28}{space 2} .0258644{col 39}{space 1}    0.30{col 48}{space 3}0.763{col 56}{space 4} -.042893{col 69}{space 3} .0584938
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks1ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0378282{col 28}{space 2} .0678458{col 39}{space 1}    0.56{col 48}{space 3}0.577{col 56}{space 4}-.0951472{col 69}{space 3} .1708036
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0182965{col 28}{space 2} .0682833{col 39}{space 1}    0.27{col 48}{space 3}0.789{col 56}{space 4}-.1155363{col 69}{space 3} .1521294
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0557096{col 28}{space 2} .0583639{col 39}{space 1}    0.95{col 48}{space 3}0.340{col 56}{space 4}-.0586816{col 69}{space 3} .1701008
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks5ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0659606{col 28}{space 2} .0671602{col 39}{space 1}    0.98{col 48}{space 3}0.326{col 56}{space 4} -.065671{col 69}{space 3} .1975922
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks6ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks7ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0628571{col 28}{space 2} .0666215{col 39}{space 1}    0.94{col 48}{space 3}0.345{col 56}{space 4}-.0677187{col 69}{space 3} .1934329
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.blocks8ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .1194321{col 28}{space 2} .0665519{col 39}{space 1}    1.79{col 48}{space 3}0.073{col 56}{space 4}-.0110072{col 69}{space 3} .2498715
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks10ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0187889{col 28}{space 2} .0566901{col 39}{space 1}    0.33{col 48}{space 3}0.740{col 56}{space 4}-.0923217{col 69}{space 3} .1298995
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks11ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0401199{col 28}{space 2} .0620595{col 39}{space 1}    0.65{col 48}{space 3}0.518{col 56}{space 4}-.0815146{col 69}{space 3} .1617543
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks12ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0035649{col 28}{space 2} .0486863{col 39}{space 1}   -0.07{col 48}{space 3}0.942{col 56}{space 4}-.0989882{col 69}{space 3} .0918584
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks13ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0852314{col 28}{space 2} .0584819{col 39}{space 1}    1.46{col 48}{space 3}0.145{col 56}{space 4} -.029391{col 69}{space 3} .1998538
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
t#c.blocks14ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.0446854{col 28}{space 2} .0534347{col 39}{space 1}   -0.84{col 48}{space 3}0.403{col 56}{space 4}-.1494155{col 69}{space 3} .0600446
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0708332{col 28}{space 2} .0787216{col 39}{space 1}    0.90{col 48}{space 3}0.368{col 56}{space 4}-.0834582{col 69}{space 3} .2251247
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .2708946{col 28}{space 2} .1385631{col 39}{space 1}    1.96{col 48}{space 3}0.051{col 56}{space 4} -.000684{col 69}{space 3} .5424732
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.father4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} -.025634{col 28}{space 2} .0620209{col 39}{space 1}   -0.41{col 48}{space 3}0.679{col 56}{space 4}-.1471927{col 69}{space 3} .0959247
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother2ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}-.1020617{col 28}{space 2} .0786856{col 39}{space 1}   -1.30{col 48}{space 3}0.195{col 56}{space 4}-.2562826{col 69}{space 3} .0521593
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother3ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}   .14285{col 28}{space 2} .0884377{col 39}{space 1}    1.62{col 48}{space 3}0.106{col 56}{space 4}-.0304847{col 69}{space 3} .3161846
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 1}t#c.mother4ce {c |}
{space 12}0  {c |}{col 16}{res}{space 2}  .040156{col 28}{space 2} .0614653{col 39}{space 1}    0.65{col 48}{space 3}0.514{col 56}{space 4}-.0803138{col 69}{space 3} .1606257
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 3}t#c.booksce {c |}
{space 12}0  {c |}{col 16}{res}{space 2} .0036495{col 28}{space 2} .0134177{col 39}{space 1}    0.27{col 48}{space 3}0.786{col 56}{space 4}-.0226487{col 69}{space 3} .0299477
{txt}{space 12}1  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .4150129{col 28}{space 2} .0319526{col 39}{space 1}   12.99{col 48}{space 3}0.000{col 56}{space 4} .3523869{col 69}{space 3} .4776389
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}teacherID{txt}: Independent{col 30}{c |}
{col 24}sd(t){col 30}{c |}{res}{col 33} 3.78e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{col 20}sd(_cons){col 30}{c |}{res}{col 33} 8.18e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{res}classID{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 3.35e-13{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} .1640991{col 44}        .{col 58}        .{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}chi2({res}3{txt}) = {res}0.00{col 59}{txt}Prob > chi2 ={col 73}{res}1.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}
({res}est4{txt} stored)

{com}. esttab using "lin2.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers ///
>          transform(ln*: exp(@) exp(@)) ///
>     eqlabels("" "sd(Intercept level at 2)" "sd(Residuals leve at 1)" "sd(Residual)", none) ///
>  varlabels(,elist(weight:_cons "{c -(}break{c )-}{c -(}hline @width{c )-}"))  
{res}{txt}(output written to {browse  `"lin2.tex"'})

{com}.         eststo clear
{txt}
{com}. 
. 
. // Analysis of the effect on grades //
. 
. // Reconstructing table A20 in appendix 12 //
. 
. // Collapse data on class level and merge with grade dataset 
. bysort classID: egen size=count(studentID)
{txt}
{com}. label define t 0 "Control" 1 "Treatment"
{txt}
{com}. preserve
{txt}
{com}. collapse (mean) interest1 values1 knowledge1 talk1 interest2 values2 knowledge2 talk2 interest3 values3 knowledge3 talk3 size mother1-mother4 father1-father4 books1-books4 books female t, by(classID)
{txt}
{com}. merge m:1 classID using "Deliberation_Grades_Data", gen(merge_grades)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}              59{txt}  (merge_grades==3)
{col 5}{hline 41}

{com}. 
. 
. // OLS-models i) unadjusted, ii) adjusted for baseline level of average knowledge
. // iii) adjusted for average knowledge & other control variables included.
. 
. local X "mother2-mother4 father2-father4 books female"
{txt}
{com}.         foreach k in index1 pass {c -(}
{txt}  2{com}.          eststo: reg `k' t, r    
{txt}  3{com}.                  gen sample1`k' = e(sample)
{txt}  4{com}. tabstat `k' if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  5{com}. drop sample1`k'
{txt}  6{com}.  eststo: reg `k' t  `X', r  
{txt}  7{com}.          gen sample1`k' = e(sample)
{txt}  8{com}. tabstat `k' if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt}  9{com}. drop sample1`k'
{txt} 10{com}.  eststo: reg `k' t  `X' knowledge1, r 
{txt} 11{com}.          gen sample1`k' = e(sample)
{txt} 12{com}. tabstat `k' if sample1`k'==1 & t==0, s(mean) format(%10.3fc)
{txt} 13{com}. drop sample1`k' 
{txt} 14{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}        57
                                                {txt}F(1, 55)          =  {res}     0.58
                                                {txt}Prob > F          = {res}    0.4485
                                                {txt}R-squared         = {res}    0.0106
                                                {txt}Root MSE          =    {res} 2.9245

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      index1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .5936783{col 26}{space 2} .7777698{col 37}{space 1}    0.76{col 46}{space 3}0.449{col 54}{space 4}-.9650072{col 67}{space 3} 2.152364
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 12.53875{col 26}{space 2} .6106486{col 37}{space 1}   20.53{col 46}{space 3}0.000{col 54}{space 4} 11.31499{col 67}{space 3} 13.76252
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:index1} {...}
{c |}{...}
{res}    12.539
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}        57
                                                {txt}F(9, 47)          =  {res}     2.08
                                                {txt}Prob > F          = {res}    0.0509
                                                {txt}R-squared         = {res}    0.3025
                                                {txt}Root MSE          =    {res} 2.6562

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      index1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .9312197{col 26}{space 2} .8072414{col 37}{space 1}    1.15{col 46}{space 3}0.255{col 54}{space 4}-.6927405{col 67}{space 3}  2.55518
{txt}{space 5}mother2 {c |}{col 14}{res}{space 2} 13.66587{col 26}{space 2}  9.08969{col 37}{space 1}    1.50{col 46}{space 3}0.139{col 54}{space 4}-4.620226{col 67}{space 3} 31.95197
{txt}{space 5}mother3 {c |}{col 14}{res}{space 2} 14.66193{col 26}{space 2} 12.59986{col 37}{space 1}    1.16{col 46}{space 3}0.250{col 54}{space 4}-10.68572{col 67}{space 3} 40.00957
{txt}{space 5}mother4 {c |}{col 14}{res}{space 2}-.6723722{col 26}{space 2} 6.172554{col 37}{space 1}   -0.11{col 46}{space 3}0.914{col 54}{space 4}-13.08995{col 67}{space 3}  11.7452
{txt}{space 5}father2 {c |}{col 14}{res}{space 2}-11.20551{col 26}{space 2} 7.845998{col 37}{space 1}   -1.43{col 46}{space 3}0.160{col 54}{space 4}-26.98962{col 67}{space 3}   4.5786
{txt}{space 5}father3 {c |}{col 14}{res}{space 2}-25.88452{col 26}{space 2} 12.30741{col 37}{space 1}   -2.10{col 46}{space 3}0.041{col 54}{space 4}-50.64384{col 67}{space 3}-1.125197
{txt}{space 5}father4 {c |}{col 14}{res}{space 2}-.9910783{col 26}{space 2}  6.48313{col 37}{space 1}   -0.15{col 46}{space 3}0.879{col 54}{space 4}-14.03345{col 67}{space 3}  12.0513
{txt}{space 7}books {c |}{col 14}{res}{space 2} 3.975154{col 26}{space 2} 1.159633{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} 1.642273{col 67}{space 3} 6.308034
{txt}{space 6}female {c |}{col 14}{res}{space 2} 1.438245{col 26}{space 2} 1.681349{col 37}{space 1}    0.86{col 46}{space 3}0.397{col 54}{space 4}-1.944194{col 67}{space 3} 4.820684
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.283857{col 26}{space 2} 3.699458{col 37}{space 1}    1.16{col 46}{space 3}0.253{col 54}{space 4}-3.158493{col 67}{space 3} 11.72621
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:index1} {...}
{c |}{...}
{res}    12.539
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}        57
                                                {txt}F(10, 46)         =  {res}     3.57
                                                {txt}Prob > F          = {res}    0.0015
                                                {txt}R-squared         = {res}    0.3637
                                                {txt}Root MSE          =    {res} 2.5644

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      index1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .5423844{col 26}{space 2} .7985541{col 37}{space 1}    0.68{col 46}{space 3}0.500{col 54}{space 4}-1.065022{col 67}{space 3}  2.14979
{txt}{space 5}mother2 {c |}{col 14}{res}{space 2} 13.62147{col 26}{space 2} 8.244694{col 37}{space 1}    1.65{col 46}{space 3}0.105{col 54}{space 4}-2.974242{col 67}{space 3} 30.21717
{txt}{space 5}mother3 {c |}{col 14}{res}{space 2} 15.63573{col 26}{space 2} 13.35343{col 37}{space 1}    1.17{col 46}{space 3}0.248{col 54}{space 4}-11.24334{col 67}{space 3}  42.5148
{txt}{space 5}mother4 {c |}{col 14}{res}{space 2} .3813881{col 26}{space 2} 5.763517{col 37}{space 1}    0.07{col 46}{space 3}0.948{col 54}{space 4}-11.21997{col 67}{space 3} 11.98275
{txt}{space 5}father2 {c |}{col 14}{res}{space 2}-10.41201{col 26}{space 2} 7.842169{col 37}{space 1}   -1.33{col 46}{space 3}0.191{col 54}{space 4}-26.19748{col 67}{space 3} 5.373453
{txt}{space 5}father3 {c |}{col 14}{res}{space 2}-18.79179{col 26}{space 2} 11.10136{col 37}{space 1}   -1.69{col 46}{space 3}0.097{col 54}{space 4}-41.13766{col 67}{space 3} 3.554079
{txt}{space 5}father4 {c |}{col 14}{res}{space 2}-.9897559{col 26}{space 2} 6.489126{col 37}{space 1}   -0.15{col 46}{space 3}0.879{col 54}{space 4}-14.05169{col 67}{space 3} 12.07218
{txt}{space 7}books {c |}{col 14}{res}{space 2} 3.004206{col 26}{space 2}  1.16203{col 37}{space 1}    2.59{col 46}{space 3}0.013{col 54}{space 4} .6651614{col 67}{space 3}  5.34325
{txt}{space 6}female {c |}{col 14}{res}{space 2} .5104976{col 26}{space 2} 1.610128{col 37}{space 1}    0.32{col 46}{space 3}0.753{col 54}{space 4}-2.730523{col 67}{space 3} 3.751518
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} 1.774153{col 26}{space 2} .5871321{col 37}{space 1}    3.02{col 46}{space 3}0.004{col 54}{space 4} .5923173{col 67}{space 3} 2.955989
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.209194{col 26}{space 2} 3.685942{col 37}{space 1}    0.33{col 46}{space 3}0.744{col 54}{space 4}-6.210222{col 67}{space 3} 8.628609
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:index1} {...}
{c |}{...}
{res}    12.539
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}        57
                                                {txt}F(1, 55)          =  {res}     0.50
                                                {txt}Prob > F          = {res}    0.4835
                                                {txt}R-squared         = {res}    0.0090
                                                {txt}Root MSE          =    {res} .13468

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        pass{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0251774{col 26}{space 2} .0356872{col 37}{space 1}    0.71{col 46}{space 3}0.483{col 54}{space 4}-.0463414{col 67}{space 3} .0966961
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8962272{col 26}{space 2} .0255298{col 37}{space 1}   35.11{col 46}{space 3}0.000{col 54}{space 4} .8450643{col 67}{space 3}   .94739
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:pass} {...}
{c |}{...}
{res}     0.896
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}        57
                                                {txt}F(9, 47)          =  {res}     1.32
                                                {txt}Prob > F          = {res}    0.2544
                                                {txt}R-squared         = {res}    0.3462
                                                {txt}Root MSE          =    {res} .11833

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        pass{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0519682{col 26}{space 2} .0356569{col 37}{space 1}    1.46{col 46}{space 3}0.152{col 54}{space 4}-.0197641{col 67}{space 3} .1237006
{txt}{space 5}mother2 {c |}{col 14}{res}{space 2} .9474101{col 26}{space 2}  .460804{col 37}{space 1}    2.06{col 46}{space 3}0.045{col 54}{space 4} .0203919{col 67}{space 3} 1.874428
{txt}{space 5}mother3 {c |}{col 14}{res}{space 2} 1.458243{col 26}{space 2} .6687406{col 37}{space 1}    2.18{col 46}{space 3}0.034{col 54}{space 4} .1129105{col 67}{space 3} 2.803576
{txt}{space 5}mother4 {c |}{col 14}{res}{space 2} .3037157{col 26}{space 2} .3068894{col 37}{space 1}    0.99{col 46}{space 3}0.327{col 54}{space 4}-.3136662{col 67}{space 3} .9210976
{txt}{space 5}father2 {c |}{col 14}{res}{space 2}-.7622138{col 26}{space 2} .3834864{col 37}{space 1}   -1.99{col 46}{space 3}0.053{col 54}{space 4}-1.533689{col 67}{space 3} .0092613
{txt}{space 5}father3 {c |}{col 14}{res}{space 2} -1.32295{col 26}{space 2} .7417987{col 37}{space 1}   -1.78{col 46}{space 3}0.081{col 54}{space 4}-2.815256{col 67}{space 3} .1693567
{txt}{space 5}father4 {c |}{col 14}{res}{space 2}  -.21459{col 26}{space 2} .3007665{col 37}{space 1}   -0.71{col 46}{space 3}0.479{col 54}{space 4}-.8196542{col 67}{space 3} .3904742
{txt}{space 7}books {c |}{col 14}{res}{space 2} .1087621{col 26}{space 2} .0498179{col 37}{space 1}    2.18{col 46}{space 3}0.034{col 54}{space 4} .0085415{col 67}{space 3} .2089828
{txt}{space 6}female {c |}{col 14}{res}{space 2}   .03336{col 26}{space 2} .0631974{col 37}{space 1}    0.53{col 46}{space 3}0.600{col 54}{space 4}-.0937766{col 67}{space 3} .1604967
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5460262{col 26}{space 2} .1516513{col 37}{space 1}    3.60{col 46}{space 3}0.001{col 54}{space 4} .2409431{col 67}{space 3} .8511093
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:pass} {...}
{c |}{...}
{res}     0.896
{txt}{hline 13}{c BT}{hline 10}

Linear regression                               Number of obs     = {res}        57
                                                {txt}F(10, 46)         =  {res}     1.36
                                                {txt}Prob > F          = {res}    0.2269
                                                {txt}R-squared         = {res}    0.3844
                                                {txt}Root MSE          =    {res} .11607

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        pass{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}t {c |}{col 14}{res}{space 2} .0378394{col 26}{space 2} .0357892{col 37}{space 1}    1.06{col 46}{space 3}0.296{col 54}{space 4}-.0342005{col 67}{space 3} .1098792
{txt}{space 5}mother2 {c |}{col 14}{res}{space 2} .9457966{col 26}{space 2} .4263474{col 37}{space 1}    2.22{col 46}{space 3}0.032{col 54}{space 4} .0876038{col 67}{space 3} 1.803989
{txt}{space 5}mother3 {c |}{col 14}{res}{space 2} 1.493628{col 26}{space 2}   .69045{col 37}{space 1}    2.16{col 46}{space 3}0.036{col 54}{space 4}  .103824{col 67}{space 3} 2.883431
{txt}{space 5}mother4 {c |}{col 14}{res}{space 2} .3420056{col 26}{space 2} .3007872{col 37}{space 1}    1.14{col 46}{space 3}0.261{col 54}{space 4}-.2634477{col 67}{space 3} .9474588
{txt}{space 5}father2 {c |}{col 14}{res}{space 2}-.7333809{col 26}{space 2} .3797403{col 37}{space 1}   -1.93{col 46}{space 3}0.060{col 54}{space 4}-1.497758{col 67}{space 3} .0309966
{txt}{space 5}father3 {c |}{col 14}{res}{space 2}-1.065226{col 26}{space 2} .6800231{col 37}{space 1}   -1.57{col 46}{space 3}0.124{col 54}{space 4}-2.434041{col 67}{space 3} .3035899
{txt}{space 5}father4 {c |}{col 14}{res}{space 2} -.214542{col 26}{space 2} .3010431{col 37}{space 1}   -0.71{col 46}{space 3}0.480{col 54}{space 4}-.8205102{col 67}{space 3} .3914263
{txt}{space 7}books {c |}{col 14}{res}{space 2} .0734813{col 26}{space 2} .0508937{col 37}{space 1}    1.44{col 46}{space 3}0.156{col 54}{space 4}-.0289624{col 67}{space 3}  .175925
{txt}{space 6}female {c |}{col 14}{res}{space 2} -.000351{col 26}{space 2} .0650192{col 37}{space 1}   -0.01{col 46}{space 3}0.996{col 54}{space 4}-.1312277{col 67}{space 3} .1305258
{txt}{space 2}knowledge1 {c |}{col 14}{res}{space 2} .0644664{col 26}{space 2}  .028851{col 37}{space 1}    2.23{col 46}{space 3}0.030{col 54}{space 4} .0063923{col 67}{space 3} .1225404
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4343039{col 26}{space 2} .1619183{col 37}{space 1}    2.68{col 46}{space 3}0.010{col 54}{space 4} .1083793{col 67}{space 3} .7602286
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{ralign 12:variable} {...}
{c |}      mean
{hline 13}{c +}{hline 10}
{ralign 12:pass} {...}
{c |}{...}
{res}     0.896
{txt}{hline 13}{c BT}{hline 10}

{com}. esttab using "Tables\grades.tex"  , keep(t _cons) label se  b(3) se(3) obslast ///
>      varwidth(13)   nogap  replace star(* 0.10 ** 0.05 *** 0.01) ///
>          title("After exp") nonumbers 
{res}{txt}(output written to {browse  `"Tables\grades.tex"'})

{com}. eststo clear 
{txt}
{com}. 
. // Posteriori power analysis referenced in appendix 12.
. pcorr index1 t mother2-mother4 father2-father4 books female knowledge1 
{txt}(obs=57)

Partial and semipartial correlations of index1 with

{col 16}Partial{col 26}Semipartial{col 43}Partial{col 53}Semipartial{col 67}Significance
   Variable {c |}{col 18}Corr.{col 32}Corr.{col 43}Corr.^2{col 57}Corr.^2{col 74}Value
{hline 12}{c +}{hline 65}
          t {c |}{res}{col 15}  0.1033{col 29}  0.0828{col 42}  0.0107{col 56}  0.0069{col 71}  0.4848
    {txt}mother2 {c |}{res}{col 15}  0.2157{col 29}  0.1762{col 42}  0.0465{col 56}  0.0311{col 71}  0.1409
    {txt}mother3 {c |}{res}{col 15}  0.2062{col 29}  0.1681{col 42}  0.0425{col 56}  0.0283{col 71}  0.1597
    {txt}mother4 {c |}{res}{col 15}  0.0097{col 29}  0.0077{col 42}  0.0001{col 56}  0.0001{col 71}  0.9478
    {txt}father2 {c |}{res}{col 15} -0.1964{col 29} -0.1598{col 42}  0.0386{col 56}  0.0255{col 71}  0.1810
    {txt}father3 {c |}{res}{col 15} -0.2736{col 29} -0.2269{col 42}  0.0749{col 56}  0.0515{col 71}  0.0598
    {txt}father4 {c |}{res}{col 15} -0.0258{col 29} -0.0206{col 42}  0.0007{col 56}  0.0004{col 71}  0.8616
      {txt}books {c |}{res}{col 15}  0.3051{col 29}  0.2555{col 42}  0.0931{col 56}  0.0653{col 71}  0.0350
     {txt}female {c |}{res}{col 15}  0.0481{col 29}  0.0384{col 42}  0.0023{col 56}  0.0015{col 71}  0.7455
 {txt}knowledge1 {c |}{res}{col 15}  0.2962{col 29}  0.2474{col 42}  0.0877{col 56}  0.0612{col 71}  0.0410
{txt}
{com}. pcorr pass t mother2-mother4 father2-father4 books female knowledge1
{txt}(obs=57)

Partial and semipartial correlations of pass with

{col 16}Partial{col 26}Semipartial{col 43}Partial{col 53}Semipartial{col 67}Significance
   Variable {c |}{col 18}Corr.{col 32}Corr.{col 43}Corr.^2{col 57}Corr.^2{col 74}Value
{hline 12}{c +}{hline 65}
          t {c |}{res}{col 15}  0.1580{col 29}  0.1256{col 42}  0.0250{col 56}  0.0158{col 71}  0.2833
    {txt}mother2 {c |}{res}{col 15}  0.3210{col 29}  0.2659{col 42}  0.1030{col 56}  0.0707{col 71}  0.0261
    {txt}mother3 {c |}{res}{col 15}  0.4064{col 29}  0.3490{col 42}  0.1652{col 56}  0.1218{col 71}  0.0042
    {txt}mother4 {c |}{res}{col 15}  0.1888{col 29}  0.1509{col 42}  0.0357{col 56}  0.0228{col 71}  0.1986
    {txt}father2 {c |}{res}{col 15} -0.2975{col 29} -0.2445{col 42}  0.0885{col 56}  0.0598{col 71}  0.0400
    {txt}father3 {c |}{res}{col 15} -0.3356{col 29} -0.2796{col 42}  0.1127{col 56}  0.0782{col 71}  0.0197
    {txt}father4 {c |}{res}{col 15} -0.1229{col 29} -0.0971{col 42}  0.0151{col 56}  0.0094{col 71}  0.4054
      {txt}books {c |}{res}{col 15}  0.1706{col 29}  0.1358{col 42}  0.0291{col 56}  0.0185{col 71}  0.2464
     {txt}female {c |}{res}{col 15} -0.0007{col 29} -0.0006{col 42}  0.0000{col 56}  0.0000{col 71}  0.9961
 {txt}knowledge1 {c |}{res}{col 15}  0.2416{col 29}  0.1953{col 42}  0.0584{col 56}  0.0381{col 71}  0.0981
{txt}
{com}. power pcorr 0.0107, ncontrol(9) alpha(.05)  
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated sample size for multiple linear regression{p_end}{txt}F test for partial correlation
{txt}{txt}{bind:Ho: rho2_p = 0}  {txt}versus  {bind:Ha: rho2_p != 0}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:power = }{res}   0.8000
{txt}{ralign 16:delta = }{res}   0.0108
{txt}{ralign 16:rho2_p = }{res}   0.0107
{txt}{ralign 16:ncontrol = }{res}        9
{txt}{ralign 16:ntested = }{res}        1

{p}{txt}Estimated sample size:{p_end}

{txt}{ralign 16:N = }{res}      728
{txt}
{com}. power pcorr 0.0250, ncontrol(9) alpha(.05)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated sample size for multiple linear regression{p_end}{txt}F test for partial correlation
{txt}{txt}{bind:Ho: rho2_p = 0}  {txt}versus  {bind:Ha: rho2_p != 0}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:power = }{res}   0.8000
{txt}{ralign 16:delta = }{res}   0.0256
{txt}{ralign 16:rho2_p = }{res}   0.0250
{txt}{ralign 16:ncontrol = }{res}        9
{txt}{ralign 16:ntested = }{res}        1

{p}{txt}Estimated sample size:{p_end}

{txt}{ralign 16:N = }{res}      309
{txt}
{com}. restore
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\xlunsi\Dropbox\VR utb reformer\DataAndPaper\Replication_log_file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 May 2019, 14:43:07
{txt}{.-}
{smcl}
{txt}{sf}{ul off}